def __init__(self, article, article_extra, abstract_sentences, vocab):
# Get ids of special tokens
start_decoding = vocab.word2id(data.START_DECODING)
stop_decoding = vocab.word2id(data.STOP_DECODING)
# Process the article
article_words = article.split()
if len(article_words) > config.max_enc_steps:
article_words = article_words[:config.max_enc_steps]
self.enc_len = len(
article_words
) # store the length after truncation but before padding
self.enc_input = [
vocab.word2id(w) for w in article_words
] # list of word ids; OOVs are represented by the id for UNK token
article_extra_words = article_extra.split()
if len(article_extra_words) > config.max_enc_steps:
article_extra_words = article_extra_words[:config.max_enc_steps]
self.enc_len_2 = len(article_extra_words)
self.enc_input_2 = [vocab.word2id(w) for w in article_extra_words]
# Process the abstract
abstract = ' '.join(abstract_sentences) # string
abstract_words = abstract.split() # list of strings
abs_ids = [
vocab.word2id(w) for w in abstract_words
] # list of word ids; OOVs are represented by the id for UNK token
# Get the decoder input sequence and target sequence
self.dec_input, self.target = self.get_dec_inp_targ_seqs(
abs_ids, config.max_dec_steps, start_decoding, stop_decoding)
self.dec_len = len(self.dec_input)
# If using pointer-generator mode, we need to store some extra info
if config.pointer_gen:
# Store a version of the enc_input where in-article OOVs are represented by their temporary OOV id; also store the in-article OOVs words themselves
self.enc_input_extend_vocab, self.article_oovs = data.article2ids(
article_words, vocab)
self.enc_input_extend_vocab_2, self.article_oovs_2 = data.article2ids(
article_extra_words, vocab)
# Get a verison of the reference summary where in-article OOVs are represented by their temporary article OOV id
abs_ids_extend_vocab = data.abstract2ids(
abstract_words, vocab, self.article_oovs + self.article_oovs_2)
# Overwrite decoder target sequence so it uses the temp article OOV ids
_, self.target = self.get_dec_inp_targ_seqs(
abs_ids_extend_vocab, config.max_dec_steps, start_decoding,
stop_decoding)
# Store the original strings
self.original_article = article
self.original_abstract = abstract
self.original_abstract_sents = abstract_sentences
def __init__(self, article, ner_path, abstract_sentences, vocab, hps):
"""Initializes the Example, performing tokenization and truncation to produce the encoder, decoder and target sequences, which are stored in self.
Args:
article: source text; a string. each token is separated by a single space.
abstract_sentences: list of strings, one per abstract sentence. In each sentence, each token is separated by a single space.
vocab: Vocabulary object
hps: hyperparameters
"""
self.hps = hps
# Get ids of special tokens
start_decoding = vocab.word2id(data.START_DECODING)
stop_decoding = vocab.word2id(data.STOP_DECODING)
# Process the article
article_words = article.split()
if len(article_words) > hps.max_enc_steps:
article_words = article_words[:hps.max_enc_steps]
self.enc_len = len(
article_words
) # store the length after truncation but before padding
self.enc_input = [
vocab.word2id(w) for w in article_words
] # list of word ids; OOVs are represented by the id for UNK token
# Process the abstract
abstract = ' '.join(abstract_sentences) # string
abstract_words = abstract.split() # list of strings
abs_ids = [
vocab.word2id(w) for w in abstract_words
] # list of word ids; OOVs are represented by the id for UNK token
# Get the decoder input sequence and target sequence
self.dec_input, self.target = self.get_dec_inp_targ_seqs(
abs_ids, hps.max_dec_steps, start_decoding, stop_decoding)
self.dec_len = len(self.dec_input)
# Process importance
self.word_id_to_imp = data.get_word_id_to_importance(ner_path, vocab)
# If using pointer-generator mode, we need to store some extra info
if hps.pointer_gen:
# Store a version of the enc_input where in-article OOVs are represented by their temporary OOV id; also store the in-article OOVs words themselves
self.enc_input_extend_vocab, self.article_oovs = data.article2ids(
article_words, vocab)
# Get a verison of the reference summary where in-article OOVs are represented by their temporary article OOV id
abs_ids_extend_vocab = data.abstract2ids(abstract_words, vocab,
self.article_oovs)
# Overwrite decoder target sequence so it uses the temp article OOV ids
_, self.target = self.get_dec_inp_targ_seqs(
abs_ids_extend_vocab, hps.max_dec_steps, start_decoding,
stop_decoding)
# Store the original strings
self.original_article = article
self.original_abstract = abstract
self.original_abstract_sents = abstract_sentences
def __init__(self, article, abstract_sentences, vocab, hps):
self.hps = hps
# Get ids of special tokens
start_decoding = vocab.word2id(data.START_DECODING)
stop_decoding = vocab.word2id(data.STOP_DECODING)
article_words = article.split()
if len(article_words) > hps.max_enc_steps:
article_words = article_words[:hps.max_enc_steps]
self.enc_len = len(article_words)
self.enc_input = [vocab.word2id(w) for w in article_words]
abstract = ' '.join(abstract_sentences)
abstract_words = abstract.split()
abs_ids = [vocab.word2id(w) for w in abstract_words]
self.dec_input, self.target = self.get_dec_inp_targ_seqs(
abs_ids, hps.max_dec_steps, start_decoding, stop_decoding)
self.dec_len = len(self.dec_input)
self.enc_input_extend_vocab, self.article_oovs = data.article2ids(
article_words, vocab)
abs_ids_extend_vocab = data.abstract2ids(abstract_words, vocab,
self.article_oovs)
_, self.target = self.get_dec_inp_targ_seqs(abs_ids_extend_vocab,
hps.max_dec_steps,
start_decoding,
stop_decoding)
self.original_article = article
self.original_abstract = abstract
self.original_abstract_sents = abstract_sentences
def __init__(self, article, abstract_sentences,pos, vocab_in,vocab_out, config,types = None):
self._config = config
# Get ids of special tokens
start_decoding = vocab_in.word2id(data.START_DECODING)
stop_decoding = vocab_in.word2id(data.STOP_DECODING)
# Process the article
article_words = article.split()
if len(article_words) > config.max_enc_steps:
article_words = article_words[:config.max_enc_steps]
self.enc_len = len(article_words) # store the length after truncation but before padding
self.enc_input = [vocab_in.word2id(w) for w in
article_words] # list of word ids; OOVs are represented by the id for UNK token
if config.use_pos_tag:
if pos==None:
self.decode_pos, self.target_pos = [],[]
self.enc_pos = []
else:
pos_words = pos.split()
if len(pos_words) > config.max_enc_steps:
pos_words = pos_words[:config.max_enc_steps]
assert len(pos_words)==len(article_words)
#self.enc_pos = [vocab_in.tag2id[w] for w in pos_words]
self.enc_pos = [vocab_out.vocab_tag.word2id(w) for w in pos_words]
self.decode_pos, self.target_pos = self.get_dec_inp_targ_seqs(self.enc_pos, config.max_dec_steps,start_decoding, stop_decoding)
if config.types:
self.types = types
# Process the abstract
abstract = ' '.join(abstract_sentences) # string
abstract_words = abstract.split() # list of strings
abs_ids = [vocab_out.word2id(w) for w in abstract_words] # list of word ids; OOVs are represented by the id for UNK token
# Get the decoder input sequence and target sequence
self.dec_input, self.target = self.get_dec_inp_targ_seqs(abs_ids, config.max_dec_steps, start_decoding,stop_decoding)
self.dec_len = len(self.dec_input)
# If using pointer-generator mode, we need to store some extra info
if config.pointer_gen:
# Store a version of the enc_input where in-article OOVs are represented by their temporary OOV id; also store the in-article OOVs words themselves
self.enc_input_extend_vocab, self.article_oovs = data.article2ids(article_words, vocab_out)
# Get a verison of the reference summary where in-article OOVs are represented by their temporary article OOV id
abs_ids_extend_vocab = data.abstract2ids(abstract_words, vocab_out, self.article_oovs)
# Overwrite decoder target sequence so it uses the temp article OOV ids
_, self.target = self.get_dec_inp_targ_seqs(abs_ids_extend_vocab, config.max_dec_steps, start_decoding,stop_decoding)
# Store the original strings
self.original_article = article
self.original_abstract = abstract
self.original_abstract_sents = abstract_sentences
def __init__(self, article, abstract_sentences, vocab):
# Get ids of special tokens
start_decoding = vocab.word2id(data.START_DECODING)
stop_decoding = vocab.word2id(data.STOP_DECODING)
# Process the article
#处理article，如果超过配置文件中的长度，截断
article_words = article.split()
if len(article_words) > config.max_enc_steps:
article_words = article_words[:config.max_enc_steps]
self.enc_len = len(
article_words
) # store the length after truncation but before padding
#编码article， 包括oov单词也得跟着编码
self.enc_input = [
vocab.word2id(w) for w in article_words
] # list of word ids; OOVs are represented by the id for UNK token
# Process the abstract
abstract = ' '.join(abstract_sentences) # string
abstract_words = abstract.split() # list of strings
#编码abstract
abs_ids = [
vocab.word2id(w) for w in abstract_words
] # list of word ids; OOVs are represented by the id for UNK token
# Get the decoder input sequence and target sequence
#构造编码阶段的输入序列和输出序列
self.dec_input, _ = self.get_dec_inp_targ_seqs(abs_ids,
config.max_dec_steps,
start_decoding,
stop_decoding)
self.dec_len = len(self.dec_input)
# If using pointer-generator mode, we need to store some extra info
# Store a version of the enc_input where in-article OOVs are represented by their temporary OOV id; also store the in-article OOVs words themselves
# 编码时需要输入原文编码和oov单词的编码
self.enc_input_extend_vocab, self.article_oovs = data.article2ids(
article_words, vocab)
# Get a verison of the reference summary where in-article OOVs are represented by their temporary article OOV id
# 获取参考摘要的id， 其中oov单词由原文中的oov单词编码表示
abs_ids_extend_vocab = data.abstract2ids(abstract_words, vocab,
self.article_oovs)
# Get decoder target sequence
# 目标编码和处理oov
_, self.target = self.get_dec_inp_targ_seqs(abs_ids_extend_vocab,
config.max_dec_steps,
start_decoding,
stop_decoding)
# Store the original strings
self.original_article = article
self.original_abstract = abstract
self.original_abstract_sents = abstract_sentences
def __init__(self, title, article, tags, abstract_sentences, abstract_sentences_all, vocab, hps, stop_words):
"""Initializes the Example, performing tokenization and truncation to produce the encoder, decoder and target sequences, which are stored in self.
Args:
article: source text; a string. each token is separated by a single space.
abstract_sentences: list of strings, one per abstract sentence. In each sentence, each token is separated by a single space.
vocab: Vocabulary object
hps: hyperparameters
"""
self.hps = hps
# Get ids of special tokens
start_decoding = vocab.word2id(data.START_DECODING)
stop_decoding = vocab.word2id(data.STOP_DECODING)
# Process the article
article_words = article.split()
if len(article_words) > hps.max_enc_steps:
article_words = article_words[:hps.max_enc_steps]
self.enc_len = len(article_words) # store the length after truncation but before padding
self.enc_input = [vocab.word2id(w) for w in article_words] # list of word ids; OOVs are represented by the id for UNK token
# Process the abstract
abstract = ' '.join(abstract_sentences) # string
abstract_words = abstract.split() # list of strings
abs_ids = [vocab.word2id(w) for w in abstract_words] # list of word ids; OOVs are represented by the id for UNK token
# Get the decoder input sequence and target sequence
self.dec_input, self.target = self.get_dec_inp_targ_seqs(abs_ids, hps.max_dec_steps, start_decoding, stop_decoding)
self.dec_len = len(self.dec_input)
if hps.title_engaged or hps.title_guided:
title_words = title.split()
self.title_input = [vocab.word2id(w) for w in title_words[:hps.max_title_len]]
self.title_len = len(self.title_input)
# If using pointer-generator mode, we need to store some extra info
if hps.pointer_gen:
# Store a version of the enc_input where in-article OOVs are represented by their temporary OOV id; also store the in-article OOVs words themselves
self.enc_input_extend_vocab, self.article_oovs = data.article2ids(article_words, vocab)
if hps.co_occurrence or hps.prev_relation or hps.co_occurrence_h or hps.co_occurrence_i or (hps.coverage and hps.coverage_weighted) or hps.attention_weighted or hps.markov_attention or hps.markov_attention_contribution:
self.cooccurrence_matrix, self.cooccurrence_weight = data.get_cooccurrence_matrix(self.enc_input_extend_vocab, win_size=hps.occurrence_window_size, exclude_words=stop_words, need_weight=(hps.co_occurrence_i or (hps.coverage and hps.coverage_weighted) or hps.attention_weighted or hps.markov_attention or hps.markov_attention_contribution), top_ten_kept=hps.top_ten_kept)
# Get a verison of the reference summary where in-article OOVs are represented by their temporary article OOV id
abs_ids_extend_vocab = data.abstract2ids(abstract_words, vocab, self.article_oovs)
# Overwrite decoder target sequence so it uses the temp article OOV ids
_, self.target = self.get_dec_inp_targ_seqs(abs_ids_extend_vocab, hps.max_dec_steps, start_decoding, stop_decoding)
if tags is not None:
self.tags = tags[:self.enc_len]
# Store the original strings
self.original_article = article
self.original_abstract = abstract
self.original_abstract_sents = abstract_sentences
self.original_abstract_sents_all = abstract_sentences_all
def __init__(self, content_text, field_text, summary_sentences, vocab, hps):
"""Initializes the Example, performing tokenization and truncation to produce the encoder, decoder and target sequences, which are stored in self.
Args:
content_text,source_text: source text; a string. each token is separated by a single space.
summary_sentences: list of strings, one per summary sentence. In each sentence, each token is separated by a single space.
vocab: Vocabulary object
hps: hyperparameters
"""
self.hps = hps
# Get ids of special tokens
start_decoding = vocab.word2id(data.START_DECODING,field=False)
stop_decoding = vocab.word2id(data.STOP_DECODING,field=False)
# Process the content_text
content_words = content_text.split()
if len(content_words) > hps.max_enc_steps:
content_words = content_words[:hps.max_enc_steps]
self.enc_len_content = len(content_words) # store the length after truncation but before padding
self.enc_input_content = [vocab.word2id(w,field=False) for w in content_words] # list of word ids; OOVs are represented by the id for UNK token
# Process the field_text
field_words = field_text.split()
if len(field_words) > hps.max_enc_steps:
field_words = field_words[:hps.max_enc_steps]
self.enc_len_field = len(content_words) # store the length after truncation but before padding
self.enc_input_field = [vocab.word2id(w,field=True) for w in field_words] # list of word ids; OOVs are represented by the id for UNK token
assert self.enc_len_field == self.enc_len_content
# Process the summary@@@@@@@@@
summary_text = ' '.join(summary_sentences) # string
summary_words = summary_text.split() # list of strings
summary_ids = [vocab.word2id(w,field = False) for w in summary_words] # list of word ids; OOVs are represented by the id for UNK token
# Get the decoder input sequence and target sequence
self.dec_input, self.target = self.get_dec_inp_targ_seqs(summary_ids, hps.max_dec_steps, start_decoding, stop_decoding)
self.dec_len = len(self.dec_input)
# If using pointer-generator mode, we need to store some extra info
if hps.pointer_gen:
# Store a version of the enc_input where in-article OOVs are represented by their temporary OOV id; also store the in-article OOVs words themselves
self.enc_input_extend_vocab, self.article_oovs = data.article2ids(content_words, vocab)
# Get a verison of the reference summary where in-article OOVs are represented by their temporary article OOV id
sum_ids_extend_vocab = data.abstract2ids(summary_words, vocab, self.article_oovs)
# Overwrite decoder target sequence so it uses the temp article OOV ids
_, self.target = self.get_dec_inp_targ_seqs(sum_ids_extend_vocab, hps.max_dec_steps, start_decoding, stop_decoding)
# Store the original strings
self.original_content = content_text
self.original_field = field_text
self.original_summary = summary_text
self.original_summary_sents = summary_sentences
def __init__(self, paragraph, question, answer, answer_positions,
vocab, max_enc_steps, max_dec_steps, dynamic_vocab=False):
self.dynamic_vocab = dynamic_vocab
# Get ids of special tokens
start_decoding = vocab.word2id(data.START_DECODING)
stop_decoding = vocab.word2id(data.STOP_DECODING)
paragraph_words = word_tokenize(paragraph)
question_words = word_tokenize(question)
answer_start_idx, answer_end_idx = answer_positions
#assert ' '.join(paragraph_words[answer_start_idx:answer_end_idx]) == answer
# Process the paragraph
if len(paragraph_words) > max_enc_steps:
if answer_end_idx <= max_enc_steps:
paragraph_words = paragraph_words[:max_enc_steps]
else:
answer_mid_idx = (answer_start_idx + answer_end_idx) // 2
# assume len(answer_words) <= len(paragraph_words)
paragraph_trunc_end = min(answer_mid_idx + max_enc_steps//2, len(paragraph_words))
paragraph_trunc_start = paragraph_trunc_end - max_enc_steps + 1
assert (paragraph_trunc_start <= answer_start_idx) and (paragraph_trunc_end >= answer_end_idx)
paragraph_words = paragraph_words[paragraph_trunc_start:paragraph_trunc_end]
answer_start_idx -= paragraph_trunc_start
answer_end_idx -= paragraph_trunc_start
self.enc_len = len(paragraph_words) # store the length after truncation but before padding
self.enc_input = [vocab.word2id(w) for w in paragraph_words] # list of word ids; OOVs are represented by the id for UNK token
# Process the abstract
question_ids = [vocab.word2id(w) for w in question_words] # list of word ids; OOVs are represented by the id for UNK token
# Get the decoder input sequence and target sequence
self.dec_input, self.target = self.get_dec_inp_targ_seqs(question_ids, max_dec_steps, start_decoding, stop_decoding)
self.dec_len = len(self.dec_input)
# If using pointer-generator mode, we need to store some extra info
if self.dynamic_vocab:
# Store a version of the enc_input where in-article OOVs are represented by their temporary OOV id; also store the in-article OOVs words themselves
self.enc_input_extend_vocab, self.enc_oovs = data.article2ids(paragraph_words, vocab)
# Get a verison of the reference summary where in-article OOVs are represented by their temporary article OOV id
question_ids_extend_vocab = data.abstract2ids(question_words, vocab, self.enc_oovs)
# Overwrite decoder target sequence so it uses the temp article OOV ids
self.dec_input_extend_vocab, self.target = self.get_dec_inp_targ_seqs(question_ids_extend_vocab, max_dec_steps, start_decoding, stop_decoding)
# Store the original strings
self.original_paragraph = paragraph
self.original_question = question
self.original_answer = answer #' '.join(paragraph_words[answer_start_idx:answer_end_idx])
self.answer_start_idx = answer_start_idx
self.answer_end_idx = answer_end_idx
def __init__(self, article, abstract_sens, vocab, hps):
"""Initializes the Example, performing tokenization and truncation to produce the encoder, decoder and target sequences.
Args:
article: source text; a string. each token is separated by a single space.
abstract_sens: list of strings, one per abstract sentence. In each sentence, each token is separated by a single space.
vocab: Vocabulary object
hps: hyperparameters
"""
self.__hps = hps
start_id = vocab.w2i(data.START_TOKEN)
stop_id = vocab.w2i(data.STOP_TOKEN)
# article
article_words = article.split()
if len(article_words) > hps.max_enc_steps:
article_words = article_words[:hps.max_enc_steps]
self.enc_len = len(article_words)
self.enc_input = [vocab.w2i(w) for w in article_words]
# abstract
abstract = ' '.join(abstract_sens)
abstract_words = abstract.split()
if len(abstract_words) > hps.max_dec_steps:
abstract_words = abstract_words[:hps.max_dec_steps]
# pointer generator
## Store a version of the enc_input where in-article OOVs are represented by their temporary OOV id and the in-article OOVs words
self.enc_input_ext_vocab, self.article_oovs = data.article2ids(
article_words, vocab)
## Get a verison of the reference summary where in-article OOVs are represented by their temporary article OOV id
abs_ids = [vocab.w2i(w) for w in abstract_words]
self.dec_input, self.dec_target = self.__get_dec_input_target_seqs(
abs_ids, hps.max_dec_steps, start_id, stop_id)
self.dec_len = len(self.dec_input)
abs_ids_ext_vocab = data.abstract2ids(abstract_words, vocab,
self.article_oovs)
_, self.ext_dec_target = self.__get_dec_input_target_seqs(
abs_ids_ext_vocab, hps.max_dec_steps, start_id, stop_id)
# origin backup
self.origin_article = article
self.origin_abstract = abstract
self.origin_abstract_sens = abstract_sens
def __init__(self, content, query, summary, vocab):
# Get ids of special tokens
start_decoding = vocab.word2id(data.START_DECODING)
stop_decoding = vocab.word2id(data.STOP_DECODING)
# Process the article
content_words = content.split()
query_words = query.split()
summary_words = summary.split()
if len(content_words) > config.max_enc_steps:
content_words = content_words[:config.max_enc_steps]
self.enc_len = len(
content_words
) # store the length after truncation but before padding
self.enc_input = [
vocab.word2id(w) for w in content_words
] # list of word ids; OOVs are represented by the id for UNK token
self.query_enc_input = [vocab.word2id(w) for w in query_words]
# Process the abstract
summary_ids = [
vocab.word2id(w) for w in summary_words
] # list of word ids; OOVs are represented by the id for UNK token
# Get the decoder input sequence and target sequence
self.dec_input, self.target = self.get_dec_inp_targ_seqs(
summary_ids, config.max_dec_steps, start_decoding, stop_decoding)
self.dec_len = len(self.dec_input)
# If using pointer-generator mode, we need to store some extra info
if config.pointer_gen:
# Store a version of the enc_input where in-article OOVs are represented by their temporary OOV id; also store the in-article OOVs words themselves
self.enc_input_extend_vocab, self.content_oovs = data.article2ids(
content_words, vocab)
# Get a verison of the reference summary where in-article OOVs are represented by their temporary article OOV id
abs_ids_extend_vocab = data.abstract2ids(summary_words, vocab,
self.content_oovs)
# Overwrite decoder target sequence so it uses the temp article OOV ids
_, self.target = self.get_dec_inp_targ_seqs(
abs_ids_extend_vocab, config.max_dec_steps, start_decoding,
stop_decoding)
# Store the original strings
self.original_content = content
self.original_query = query
self.original_summary = summary
def __init__(self, article, abstract_sentences, vocab):
# Get ids of special tokens
start_decoding = vocab.word2id(data.START_DECODING)
stop_decoding = vocab.word2id(data.STOP_DECODING)
# 处理article，如果超过配置文件中的长度，截断。
article_words = list(
article
) #*******************************************************************中文分词
if len(article_words) > config.max_enc_steps:
article_words = article_words[:config.max_enc_steps]
self.enc_len = len(
article_words
) # store the length after truncation but before padding
# 编码 article，包括oov单词也得跟着编码
self.enc_input = [vocab.word2id(w) for w in article_words]
# 处理 abstract
abstract = ' '.join(abstract_sentences) # string
abstract_words = list(abstract) # list of strings
# 编码 abstract
abs_ids = [vocab.word2id(w) for w in abstract_words] #
# 构建解码阶段的输入序列和输出序列“strat w1 w2”, "w1 w2 end",要一样长
self.dec_input, self.target = self.get_dec_inp_targ_seqs(
abs_ids, config.max_dec_steps, start_decoding, stop_decoding)
self.dec_len = len(self.dec_input)
# 如果使用pointer-generator模式, 需要一些额外信息
if config.pointer_gen:
# 编码时需要输入原文编码和oov单词的编码
self.enc_input_extend_vocab, self.article_oovs = data.article2ids(
article_words, vocab)
# 获取参考摘要的id，其中oov单词由原文中的oov单词编码表示
abs_ids_extend_vocab = data.abstract2ids(abstract_words, vocab,
self.article_oovs)
# 目标编码和处理oov
_, self.target = self.get_dec_inp_targ_seqs(
abs_ids_extend_vocab, config.max_dec_steps, start_decoding,
stop_decoding)
# 存储原始数据
self.original_article = article
self.original_abstract = abstract
# 编码前的摘要，单词列表
self.original_abstract_sents = abstract_sentences
def genrate_Input(text, hps, vocab):
class Data:
pass
# Process the article
article_words = text.lower()
# for large text divide into equal parts
article_wordsArray = genrateArray(article_words, hps.max_enc_steps)
input_dataArray = []
for article_sent in article_wordsArray:
article_words = article_sent.split()
input_data = Data()
if len(article_words) > hps.max_enc_steps:
article_words = article_words[:hps.max_enc_steps]
enc_len = len(article_words
) # store the length after truncation but before padding
enc_input = [
vocab.word2id(w) for w in article_words
] # list of word ids; OOVs are represented by the id for UNK token
enc_input_extend_vocab, article_oovs = article2ids(
article_words, vocab)
max_enc_seq_len = enc_len
input_data.enc_batch = np.zeros((hps.batch_size, max_enc_seq_len),
dtype=np.int32)
input_data.enc_lens = np.zeros((hps.batch_size), dtype=np.int32)
input_data.enc_padding_mask = np.zeros(
(hps.batch_size, max_enc_seq_len), dtype=np.float32)
input_data.enc_batch_extend_vocab = np.zeros(
(hps.batch_size, max_enc_seq_len), dtype=np.int32)
input_data.max_art_oovs = len(article_oovs)
input_data.batch_reward = np.zeros((hps.batch_size, hps.max_dec_steps),
dtype=np.float32)
input_data.batch_rouge_reward = np.zeros((hps.batch_size, 1),
dtype=np.float32)
input_data.art_oovs = [article_oovs]
input_data.min_dec_steps = enc_len / 10
input_data.max_dec_steps = min(hps.max_dec_steps, enc_len / 2)
input_data.enc_batch[0] = enc_input
input_data.enc_lens[0] = enc_len
input_data.enc_batch_extend_vocab[0, :] = enc_input_extend_vocab[:]
for j in range(enc_len):
input_data.enc_padding_mask[0][j] = 1
input_dataArray.append(input_data)
return input_dataArray
def __init__(self, article, abstract_sentences, vocab, hps):
"""Initializes the Example, performing tokenization and truncation to produce the encoder, decoder and target sequences, which are stored in self.
Args:
article: source text; a string. each token is separated by a single space.
abstract_sentences: list of strings, one per abstract sentence. In each sentence, each token is separated by a single space.
vocab: Vocabulary object
hps: hyperparameters
"""
self.hps = hps
# Get ids of special tokens
start_decoding = vocab.word2id(data.START_DECODING)
stop_decoding = vocab.word2id(data.STOP_DECODING)
# Process the article
article_words = article.split()
if len(article_words) > hps.max_enc_steps:
article_words = article_words[:hps.max_enc_steps]
self.enc_len = len(article_words) # store the length after truncation but before padding
self.enc_input = [vocab.word2id(w) for w in article_words] # list of word ids; OOVs are represented by the id for UNK token
# Process the abstract
abstract = ' '.join(abstract_sentences) # string
abstract_words = abstract.split() # list of strings
abs_ids = [vocab.word2id(w) for w in abstract_words] # list of word ids; OOVs are represented by the id for UNK token
# Get the decoder input sequence and target sequence
self.dec_input, self.target = self.get_dec_inp_targ_seqs(abs_ids, hps.max_dec_steps, start_decoding, stop_decoding)
self.dec_len = len(self.dec_input)
# If using pointer-generator mode, we need to store some extra info
if hps.pointer_gen:
# Store a version of the enc_input where in-article OOVs are represented by their temporary OOV id; also store the in-article OOVs words themselves
self.enc_input_extend_vocab, self.article_oovs = data.article2ids(article_words, vocab)
# Get a verison of the reference summary where in-article OOVs are represented by their temporary article OOV id
abs_ids_extend_vocab = data.abstract2ids(abstract_words, vocab, self.article_oovs)
# Overwrite decoder target sequence so it uses the temp article OOV ids
_, self.target = self.get_dec_inp_targ_seqs(abs_ids_extend_vocab, hps.max_dec_steps, start_decoding, stop_decoding)
# Store the original strings
self.original_article = article
self.original_abstract = abstract
self.original_abstract_sents = abstract_sentences
def __init__(self, article, abstract, vocab, hps):
"""
"""
self.hps = hps
start_id = vocab._word2id(data.DECODING_START)
end_id = vocab._word2id(data.DECODING_END)
article_words = article.split()
if len(article_words) > hps.max_enc_steps:
article_words = article_words[:hps.max_enc_steps]
self.enc_len = len(article_words)
self.enc_input = [vocab._word2id(w) for w in article_words]
abstract_words = abstract.split()
abs_ids = [vocab._word2id(w) for w in abstract_words]
self.dec_input = [start_id] + abs_ids
self.dec_target = abs_ids + [end_id]
self.pad_id = vocab._word2id(data.PAD_TOKEN)
if hps.pointer:
self.enc_input_extend_vocab, self.article_oovs = data.article2ids(
article_words, vocab)
abs_ids_extend_vocab = data.abstract2ids(abstract_words, vocab,
self.article_oovs)
self.dec_target = abs_ids_extend_vocab + [end_id]
while len(self.enc_input_extend_vocab) < hps.max_enc_steps:
self.enc_input_extend_vocab.append(self.pad_id)
while len(self.enc_input) < hps.max_enc_steps:
self.enc_input.append(self.pad_id)
while len(self.dec_input) < hps.max_dec_steps:
self.dec_input.append(self.pad_id)
self.dec_target.append(self.pad_id)
if len(self.dec_input) > hps.max_dec_steps:
self.dec_input = self.dec_input[:hps.max_dec_steps - 1]
self.dec_target = self.dec_target[:hps.max_dec_steps - 1]
self.dec_len = len(self.dec_input)
self.original_article = article
self.original_abstract = abstract
def __init__(self, article, abstract, vocab):
# 开始、结束 ID
start_decoding = vocab.word2id(data.START_DECODING)
stop_decoding = vocab.word2id(data.STOP_DECODING)
article_words = article.split()
if len(article_words) > config.max_enc_steps:
article_words = article_words[:config.max_enc_steps]
self.enc_len = len(article_words)
# 编码 article，不在vocab的用UNK序号表示，拿这个做输入
self.enc_input = [vocab.word2id(w) for w in article_words]
# 处理 abstract，不在vocab的用UNK序号表示
abstract_words = abstract.split()
abs_ids = [vocab.word2id(w) for w in abstract_words]
# 解码阶段的输入序列和输出序列
self.dec_input, self.target = self.get_dec_inp_targ_seqs(
abs_ids, config.max_dec_steps, start_decoding, stop_decoding)
self.dec_len = len(self.dec_input)
# 如果使用pointer-generator模式,enc_input中unk对应的0序号，会替换为词汇表长+oov词汇表内位置的序号
if config.pointer_gen:
# 编码输入扩展了oov词的序列（unk的有了序号）和oov单词
self.enc_input_extend_vocab, self.article_oovs = data.article2ids(
article_words, vocab)
# 获取参考摘要的id，其中oov单词由原文中的oov单词编码表示
abs_ids_extend_vocab = data.abstract2ids(abstract_words, vocab,
self.article_oovs)
# 新的目标序列，unk词有序号
_, self.target = self.get_dec_inp_targ_seqs(
abs_ids_extend_vocab, config.max_dec_steps, start_decoding,
stop_decoding)
# 存储原始数据
self.original_article = article
self.original_abstract = abstract
# 编码前的摘要，单词列表
self.original_abstract_sents = abstract
def __init__(self, article, abstract_sentences, vocab):
# Get ids of special tokens
start_decoding = vocab.word2id(data.START_DECODING)
stop_decoding = vocab.word2id(data.STOP_DECODING)
# Process the article
article_words = article.split()
if len(article_words) > config.max_enc_steps:
article_words = article_words[:config.max_enc_steps]
self.enc_len = len(article_words) # store the length after truncation but before padding
self.enc_input = [vocab.word2id(w) for w in article_words] # list of word ids; OOVs are represented by the id for UNK token
# Process the abstract
abstract = ' '.join(abstract_sentences) # string
abstract_words = abstract.split() # list of strings
abs_ids = [vocab.word2id(w) for w in abstract_words] # list of word ids; OOVs are represented by the id for UNK token
# Get the decoder input sequence and target sequence
self.dec_input, self.target = self.get_dec_inp_targ_seqs(abs_ids, config.max_dec_steps, start_decoding, stop_decoding)
self.dec_len = len(self.dec_input)
# If using pointer-generator mode, we need to store some extra info
if config.pointer_gen:
# Store a version of the enc_input where in-article OOVs are represented by their temporary OOV id; also store the in-article OOVs words themselves
self.enc_input_extend_vocab, self.article_oovs = data.article2ids(article_words, vocab)
# Get a verison of the reference summary where in-article OOVs are represented by their temporary article OOV id
abs_ids_extend_vocab = data.abstract2ids(abstract_words, vocab, self.article_oovs)
# Overwrite decoder target sequence so it uses the temp article OOV ids
_, self.target = self.get_dec_inp_targ_seqs(abs_ids_extend_vocab, config.max_dec_steps, start_decoding, stop_decoding)
# Store the original strings
self.original_article = article
self.original_abstract = abstract
self.original_abstract_sents = abstract_sentences
def __init__(self, reviews, ratings, answer_sentences, question, label,
vocab, hps):
"""Initializes the Example, performing tokenization and truncation to produce the encoder, decoder and target sequences, which are stored in self.
Args:
reviews: review text; a list.
ratings: alist.
answer_sentences: list of strings, one per abstract sentence. In each sentence, each token is separated by a single space.
vocab: Vocabulary object
hps: hyperparameters
"""
self.hps = hps
# Get ids of special tokens
start_decoding = vocab.word2id(data.START_DECODING)
stop_decoding = vocab.word2id(data.STOP_DECODING)
# Process the reviews
self.r_lens = []
self.r_batch = []
self.rating_batch = ratings
reviews_words = []
for review in reviews:
review_words = review.split()
if len(review_words) > hps.max_enc_steps:
review_words = review_words[:hps.max_enc_steps]
reviews_words.append(review_words)
self.r_lens.append(
len(review_words
)) # store the length after truncation but before padding
self.r_batch.append(
[vocab.word2id(w) for w in review_words]
) # list of word ids; OOVs are represented by the id for UNK token
# Process the abstract
answer = ' '.join(answer_sentences) # string
answer_words = answer.split() # list of strings
ans_ids = [
vocab.word2id(w) for w in answer_words
] # list of word ids; OOVs are represented by the id for UNK token
# Process the question
question_words = question.split()
self.q_lens = len(question_words)
self.q_batch = [vocab.word2id(w) for w in question_words]
# Process the label
self.y_target = label
# Get the decoder input sequence and target sequence
self.dec_input, self.target = self.get_dec_inp_targ_seqs(
ans_ids, hps.max_dec_steps, start_decoding, stop_decoding)
self.dec_len = len(self.dec_input)
assert self.dec_len > 0
# If using pointer-generator mode, we need to store some extra info
if hps.pointer_gen:
self.oovs = []
# Store a version of the enc_input where in-article OOVs are represented by their temporary OOV id; also store the in-article OOVs words themselves
self.reviews_extend_vocab = []
for review_words in reviews_words:
review_extend_vocab, self.oovs = data.article2ids(
review_words, vocab, self.oovs)
self.reviews_extend_vocab.append(review_extend_vocab)
# question OOV id.
self.question_extend_vocab, self.oovs = data.article2ids(
question_words, vocab, self.oovs)
# Get a verison of the reference summary where in-article OOVs are represented by their temporary article OOV id
ans_ids_extend_vocab = data.abstract2ids(answer_words, vocab,
self.oovs)
# Overwrite decoder target sequence so it uses the temp article OOV ids
_, self.target = self.get_dec_inp_targ_seqs(
ans_ids_extend_vocab, hps.max_dec_steps, start_decoding,
stop_decoding)
# Store the original strings
self.original_reviews = reviews
self.original_answer = answer
self.original_answer_sents = answer_sentences
self.original_question = question
def __init__(self, article_sentences, extract_ids, abstract_sentences,
vocab, hps):
"""Initializes the Example, performing tokenization and truncation to produce the encoder, decoder and target sequences, which are stored in self.
Args:
article: source text; a string. each token is separated by a single space.
abstract_sentences: list of strings, one per abstract sentence. In each sentence, each token is separated by a single space.
vocab: Vocabulary object
hps: hyperparameters
"""
self.hps = hps
# Store the original strings
self.original_article_sents = article_sentences
self.original_extract_ids = extract_ids
self.original_abstract_sents = abstract_sentences
if hps.model in ['rewriter', 'end2end']:
# Get ids of special tokens
start_decoding = vocab.word2id(data.START_DECODING)
stop_decoding = vocab.word2id(data.STOP_DECODING)
if hps.model == 'rewriter':
# Process the extracted sentences
extract_sentences = [
article_sentences[idx] for idx in extract_ids
]
enc_input_words = ' '.join(extract_sentences).split()
else:
# Process the article sentences
enc_input_words = ' '.join(article_sentences).split()
self.enc_input_sent_ids = []
for idx, sent in enumerate(article_sentences):
sent_words = sent.split()
for _ in range(len(sent_words)):
if len(self.enc_input_sent_ids) < hps.max_enc_steps:
self.art_len = idx + 1
self.enc_input_sent_ids.append(idx)
if len(enc_input_words) > hps.max_enc_steps:
enc_input_words = enc_input_words[:hps.max_enc_steps]
self.enc_len = len(
enc_input_words
) # store the length after truncation but before padding
self.enc_input = [
vocab.word2id(w) for w in enc_input_words
] # list of word ids; OOVs are represented by the id for UNK token
# Process the abstract
abstract = ' '.join(abstract_sentences) # string
abstract_words = abstract.split() # list of strings
abs_ids = [
vocab.word2id(w) for w in abstract_words
] # list of word ids; OOVs are represented by the id for UNK token
# Get the decoder input sequence and target sequence
self.dec_input, self.target = self.get_dec_inp_targ_seqs(
abs_ids, hps.max_dec_steps, start_decoding, stop_decoding)
self.dec_len = len(self.dec_input)
# Store a version of the enc_input where in-article OOVs are represented by their temporary OOV id; also store the in-article OOVs words themselves
self.enc_input_extend_vocab, self.article_oovs = data.article2ids(
enc_input_words, vocab)
# Get a verison of the reference summary where in-article OOVs are represented by their temporary article OOV id
abs_ids_extend_vocab = data.abstract2ids(abstract_words, vocab,
self.article_oovs)
# Overwrite decoder target sequence so it uses the temp article OOV ids
_, self.target = self.get_dec_inp_targ_seqs(
abs_ids_extend_vocab, hps.max_dec_steps, start_decoding,
stop_decoding)
if hps.model in ['selector', 'end2end']:
# Process the article
if hps.model == 'selector':
if len(article_sentences) > hps.max_art_len:
article_sentences = article_sentences[:hps.max_art_len]
self.art_len = len(
article_sentences
) # store the length after truncation but before padding
elif hps.model == 'end2end':
if self.art_len > hps.max_art_len:
self.art_len = hps.max_art_len
article_sentences = article_sentences[:self.art_len]
self.art_ids = []
self.sent_lens = []
for sent in article_sentences:
sent = sent.split()
if len(sent) > hps.max_sent_len:
sent = sent[:hps.max_sent_len]
self.sent_lens.append(len(sent))
self.art_ids.append([vocab.word2id(w) for w in sent])
def __init__(self, article, abstract_sentences, vocab, concept_vocab):
start_decoding = vocab.word2id(data.START_DECODING)
stop_decoding = vocab.word2id(data.STOP_DECODING)
article = ' '.join(article)
article_words = article.split()
if len(article_words) > config.max_enc_steps:
article_words = article_words[:config.max_enc_steps]
self.enc_len = len(article_words)
self.enc_input = [vocab.word2id(w) for w in article_words]
abstract = ' '.join(abstract_sentences)
abstract_words = abstract.split()
abs_ids = [vocab.word2id(w) for w in abstract_words]
self.dec_input, self.target = self.get_dec_inp_targ_seqs(
abs_ids, config.max_dec_steps, start_decoding, stop_decoding)
self.dec_len = len(self.dec_input)
if config.pointer_gen:
self.enc_input_extend_vocab, self.article_oovs, self.enc_input_concept_extend_vocab, self.concept_p, self.position, self.concept_mask = data.article2ids(
article_words, vocab, concept_vocab)
abs_ids_extend_vocab = data.abstract2ids(abstract_words, vocab,
self.article_oovs)
_, self.target = self.get_dec_inp_targ_seqs(
abs_ids_extend_vocab, config.max_dec_steps, start_decoding,
stop_decoding)
self.original_article = article
self.original_abstract = abstract
self.original_abstract_sents = abstract_sentences
def __init__(self, article, abstract_sentences, article_id, sections, section_names, labels, vocab, hps):
"""Initializes the Example, performing tokenization and truncation to produce the encoder, decoder and target sequences, which are stored in self.
Args:
article: source text; a list of strings. each token is separated by a single space.
abstract_sentences: list of strings, one per abstract sentence. In each sentence, each token is separated by a single space.
article_id: string
sections: list of list of strings
section_names: list of strings
labels: list of strings, for extractive summarization training (TODO Later)
vocab: Vocabulary object
hps: hyperparameters
"""
self.hps = hps
self.discard = False
# Get ids of special tokens
start_decoding = vocab.word2id(data.START_DECODING)
stop_decoding = vocab.word2id(data.STOP_DECODING)
# clean section information
# clean sections after conclusions
if hps.hier:
end_loc = len(section_names)
beg_loc = 0
for i,s in enumerate(section_names):
if 'conclu' in s.lower():
end_loc = i + 1
if 'intro' in s.lower() and beg_loc == 0:
beg_loc = i
if beg_loc < len(section_names) - end_loc:
sections = sections[beg_loc:end_loc]
try:
intro_last = sections[beg_loc][-2:] # last two sentences in the intro
except IndexError:
# print('article_id: {}, len(sections): {}, section_names: {}'.format(article_id, len(sections), section_names))
self.discard = True
return
# intro_first = []
i = 0
# intro_last_len = _count_words(intro_last)
# intro_len = intro_last_len
# while(intro_len < hps.max_intro_len):
# intro_first.append(sections[beg_loc][i])
# intro_len = _count_words(intro_first) + intro_last_len
# i += 1
if not hps.split_intro:
max_sents = hps.max_intro_sents - 2 # exclude the last two sents
intro_first = sections[beg_loc][:max_sents]
intro_last_words = _get_section_words(intro_last, pad=False)
intro_last_len = len(intro_last_words) # flatten list of sents, get the string inside, count words
discard_last = False
if intro_last_len > hps.max_intro_len:
discard_last = True
len_limit = hps.max_intro_len - intro_last_len if not discard_last else hps.max_intro_len
# truncate the intro by len_limit (we consider last 2 sentences from the intro to be there always)
# Flatten list of lists, get the first element (string), get words, get first n words, return a striing, make it a list, extend it with intro_last
intro_words = _get_section_words(intro_first, len_limit, pad=False)
try:
if intro_words[-1] != '.':
intro_words = intro_words[:-1] + ['.']
if not discard_last:
intro_words += intro_last_words
intro_words = _pad_words(intro_words, hps.max_intro_len)
except IndexError:
print('No first section, Example discarded: ', article_id)
self.discard = True
else:
intro_first = sections[beg_loc][:hps.max_intro_sents]
intro_words = _get_section_words(intro_first, hps.max_intro_len, pad=True)
try:
conclusion_words = _get_section_words(sections[end_loc - beg_loc - 1][:hps.max_conclusion_sents], hps.max_conclusion_len)
except:
import pdb; pdb.set_trace()
print("ERROR, pause and check")
print('end_loc:', end_loc)
print('section_names:', section_names)
print('num_sections: {}'.format(len(sections)))
print('len_sections_sents:', [len(e) for e in sections])
# if not hps.intro_split:
article_sections = [_get_section_words(s[:hps.max_section_sents], hps.max_section_len)
for s in sections[1:-1][:hps.num_sections - 2]]
# else:
# tmp_sections = []
# remaining_sec = sections[1:-1]
# if len(remaining_sec) > hps.num_sections - 2:
# for i in range(hps.num_sections - 2):
# tmp_sections.append(remaining_sec[i])
# last_sec = []
# while(i < len(remaining_sec)):
# last_sec.extend(remaining_sec[i])
# i += 1
# tmp_sections.append(last_sec)
# remaining_sec = tmp_sections
#
# article_sections = [_get_section_words(s, hps.max_section_len)
# for s in remaining_sec]
sections = [intro_words] + article_sections + [conclusion_words]
sec_len = len(sections)
self.sec_len = sec_len
self.num_words_section = [hps.max_section_len for e in sections]
self.num_words_section_nopad = [len(e) for e in sections]
# TODO: Assumption is that sections is a list of list (sections, sentences), check if assumption is true
# TODO: Assumtpion is that number of sections is greater than 2, check if assumption is true
# pad_id = vocab.word2id(data.PAD_TOKEN)
article_text = ' '.join(article)
# Process the article
article_words = article_text.split()
if len(article_words) > hps.max_enc_steps:
article_words = article_words[:hps.max_enc_steps]
# store the length after truncation but before padding
self.enc_len = len(article_words)
# list of word ids; OOVs are represented by the id for UNK token
self.enc_input = [vocab.word2id(w) for w in article_words]
if hps.hier:
self.enc_sections = []
for sec in sections:
self.enc_sections.append([vocab.word2id(w) for w in sec])
self.enc_sec_len = [len(e) for e in self.enc_sections]
# self.enc_sec_len = sec_len # TODO: Check
# Process the abstract
abstract = ' '.join(abstract_sentences) # string
abstract_words = abstract.split() # list of strings
# list of word ids; OOVs are represented by the id for UNK token
abs_ids = [vocab.word2id(w) for w in abstract_words]
# Get the decoder input sequence and target sequence
self.dec_input, self.target = self.get_dec_inp_targ_seqs(
abs_ids, hps.max_dec_steps, start_decoding, stop_decoding)
self.dec_len = len(self.dec_input)
# If using pointer-generator mode, we need to store some extra info
if hps.pointer_gen:
# Store a version of the enc_input where in-article OOVs are
# represented by their temporary OOV id; also store the in-article
# OOVs words themselves
self.enc_input_extend_vocab, self.article_oovs = data.article2ids(
article_words, vocab)
# Get a verison of the reference summary where in-article OOVs are
# represented by their temporary article OOV id
abs_ids_extend_vocab = data.abstract2ids(
abstract_words, vocab, self.article_oovs)
# Overwrite decoder target sequence so it uses the temp article OOV
# ids, the target now includes words that are in the article but
# not in the abstract, so represented as OOV
_, self.target = self.get_dec_inp_targ_seqs(
abs_ids_extend_vocab, hps.max_dec_steps, start_decoding, stop_decoding)
self.article_id = article_id
self.sections = sections
self.section_names = section_names
self.labels = labels
# Store the original strings
self.original_article = article
self.original_abstract = abstract
self.original_abstract_sents = abstract_sentences
def __init__(self, article, abstract, vocab, hps):
"""
Initializes the Example, performing tokenization and truncation to produce the encoder,
decoder and target sequences, which are stored in self.
Args:
article: source text; a string. each token is separated by a single space.
abstract: reference summary; a string. each token is separated by a single space.
vocab: Vocabulary object
hps: hyperparameters
"""
self.hps = hps
# Get ids of special tokens
start_decoding = vocab.word2id(data.START_DECODING, None)
stop_decoding = vocab.word2id(data.STOP_DECODING, None)
# Process the article
article_words = [data.parse_word(word) for word in article.split()]
if len(article_words) > hps.max_enc_steps:
article_words = article_words[:hps.max_enc_steps]
# Store the length after truncation but before padding
self.enc_len = len(article_words)
# List of word ids; OOVs and entities are represented by ids less than data.N_FREE_TOKENS
self.enc_input = [
vocab.word2id(w, word_type) for w, word_type in article_words
]
# Process the abstract
abstract_words = [data.parse_word(word) for word in abstract.split()]
# List of word ids; OOVs and entities are represented by ids less than data.N_FREE_TOKENS
abs_ids = [
vocab.word2id(w, word_type) for w, word_type in abstract_words
]
# Get the decoder input sequence and target sequence with non-article specific ids.
self.dec_input, target_orig = self.get_dec_inp_targ_seqs(
abs_ids, hps.max_dec_steps, start_decoding, stop_decoding)
self.dec_len = len(self.dec_input)
# Store a version of the enc_input where in-article OOVs and entities are represented by
# their temporary OOV id. Also store the in-article OOVs words themselves and a mapping
# from temporary OOV ids to vocab ids.
self.enc_input_extend_vocab, self.article_oovs, self.article_id_to_word_id = (
data.article2ids(article_words, vocab, hps.copy_only_entities))
# Get set of words that can be copied.
if hps.copy_only_entities:
# article_oovs only has entities
copyable_words = set(self.article_oovs)
else:
copyable_words = set([w for w, word_type in article_words])
# Get a version of the reference summary where in-article OOVs are represented by their
# temporary article OOV id
abs_ids_extend_vocab = data.abstract2ids(abstract_words, vocab,
self.article_oovs,
copyable_words,
hps.output_vocab_size)
# Set decoder target sequence that uses the temp article OOV ids
_, self.target = self.get_dec_inp_targ_seqs(abs_ids_extend_vocab,
hps.max_dec_steps,
start_decoding,
stop_decoding)
# Compute a mask for which tokens are people.
people_tokens = {
vocab.word2id('', token)
for token in data.PERSON_TOKENS
}
self.target_people = [
float(token in people_tokens) for token in target_orig
]
# Get list of people ids
self.people_ids = []
for article_id, word_id in self.article_id_to_word_id.iteritems():
if word_id in people_tokens:
self.people_ids.append(article_id)
# Store the original strings
self.original_article = article
self.original_abstract = abstract
def __init__(self, article, abstract_sentences, vocab, hps, word_edge_list=None, query=None, query_edge_list=None, epoch_num=None, bert_vocab=None):
"""Initializes the Example, performing tokenization and truncation to produce the encoder, decoder and target sequences, which are stored in self.
Args:
article: source text; a string. each token is separated by a single space.
abstract_sentences: list of strings, one per abstract sentence. In each sentence, each token is separated by a single space.
vocab: Vocabulary object
hps: hyperparameters
"""
self.hps = hps
# Get ids of special tokens
start_decoding = vocab.word2id(data.START_DECODING)
stop_decoding = vocab.word2id(data.STOP_DECODING)
self.bert_vocab = bert_vocab
self.epoch_num = epoch_num #deprecated
self.enc_pos_offset = None
self.query_pos_offset = None
# Process the article
article_words = article.split()
if len(article_words) > hps.max_enc_steps.value:
article_words = article_words[:hps.max_enc_steps.value]
self.enc_len = len(article_words) # store the length after truncation but before padding
self.enc_input = [vocab.word2id(w) for w in
article_words] # list of word ids; OOVs are represented by the id for UNK token
#tf.logging.info(self.enc_len)
if self.hps.use_elmo.value:
self.enc_input_raw = article_words
# Process the abstract
abstract = ' '.join(abstract_sentences) # string
abstract_words = abstract.split() # list of strings
abs_ids = [vocab.word2id(w) for w in
abstract_words] # list of word ids; OOVs are represented by the id for UNK token
# Process the query
if hps.query_encoder.value:
query_words = query.split()
#query_words = word_features.get_tokens(query)
if len(query_words) > hps.max_query_steps.value:
#tf.logging.info('Before_query: %d Hps: %d'%(len(query_words),hps.max_query_steps.value))
query_words = query_words[len(query_words)- hps.max_query_steps.value:]
#tf.logging.info('Big_query : %d'%(len(query_words)))
query = " ".join(q for q in query_words)
self.query_len = len(query_words) # store the length after truncation but before padding
self.query_input = [vocab.word2id(w) for w in query_words] # list of word ids; OOVs are represented by the id for UNK token
if self.hps.use_query_elmo.value:
self.query_input_raw = query_words #tensorflow_hub requires raw text
# Get the decoder input sequence and target sequence
self.dec_input, self.target = self.get_dec_inp_targ_seqs(abs_ids, hps.max_dec_steps.value, start_decoding,
stop_decoding)
self.dec_len = len(self.dec_input)
# If using pointer-generator mode, we need to store some extra info
if hps.pointer_gen.value:
# Store a version of the enc_input where in-article OOVs are represented by their temporary OOV id; also store the in-article OOVs words themselves
self.enc_input_extend_vocab, self.article_oovs = data.article2ids(article_words, vocab)
# Get a verison of the reference summary where in-article OOVs are represented by their temporary article OOV id
abs_ids_extend_vocab = data.abstract2ids(abstract_words, vocab, self.article_oovs)
# Overwrite decoder target sequence so it uses the temp article OOV ids
_, self.target = self.get_dec_inp_targ_seqs(abs_ids_extend_vocab, hps.max_dec_steps.value, start_decoding,
stop_decoding)
if hps.word_gcn.value:
self.word_edge_list = word_edge_list
if hps.query_gcn.value:
self.query_edge_list = query_edge_list
if hps.use_bert.value:
self.enc_input, self.enc_pos_offset = bert_vocab.convert_glove_to_bert_indices(self.enc_input)
self.enc_len = len(self.enc_input)
if hps.use_query_bert.value:
self.query_input, self.query_pos_offset = bert_vocab.convert_glove_to_bert_indices(self.query_input)
self.query_len = len(self.query_input)
# Store the original strings
self.original_article = article
self.original_abstract = abstract
self.original_abstract_sents = abstract_sentences
#if hps.query_encoder:
self.original_query = query
def __init__(self, article, abstract_sentences, word2topic_dist,
topic_dist, vocab, hps):
"""Initializes the Example, performing tokenization and truncation to produce the encoder, decoder and target sequences, which are stored in self.
Args:
article: source text; a string. each token is separated by a single space.
abstract_sentences: list of strings, one per abstract sentence. In each sentence, each token is separated by a single space.
vocab: Vocabulary object
hps: hyperparameters
"""
self.hps = hps
# Get ids of special tokens
start_decoding = vocab.word2id(data.START_DECODING)
stop_decoding = vocab.word2id(data.STOP_DECODING)
# Process the article
article_words = article.split(' ')
if len(article_words) > hps.max_enc_steps:
article_words = article_words[:hps.max_enc_steps]
self.enc_len = len(
article_words
) # store the length after truncation but before padding
self.enc_input = [
vocab.word2id(w) for w in article_words
] # list of word ids; OOVs are represented by the id for UNK token
# Process the abstract
abstract = ' '.join(abstract_sentences) # string
abstract_words = abstract.split(' ') # list of strings
abs_ids = [
vocab.word2id(w) for w in abstract_words
] # list of word ids; OOVs are represented by the id for UNK token
# Process the word2topic distribution
if FLAGS.word2topic:
self.word2topic_enc_input = [] #list of word2topic_value list
topic_words = word2topic_dist.split(' ')
if len(topic_words) > hps.max_enc_steps:
topic_words = topic_words[:hps.max_enc_steps]
for w_dist in topic_words:
if w_dist == '[stopwords]':
self.word2topic_enc_input.append([float(0)] *
hps.word2topic_emb_dim)
elif len(w_dist.split('/')) == hps.word2topic_emb_dim:
_enc_input = w_dist.split('/')
self.word2topic_enc_input.append(
[float(v) for v in _enc_input])
else:
print('Invalid word2topic input embedding')
self.word2topic_enc_input.append([float(0)] *
hps.word2topic_emb_dim)
self.word2topic_enc_len = len(
self.word2topic_enc_input
) # store the length of word2topic encoder
if self.enc_len != self.word2topic_enc_len:
print('Length of word2topic encoder is error!')
# Process the topic distribution
if FLAGS.topic:
self.topic_enc_input = [] #list of topic_id
self.topic_enc_weight = [] #list of weight for each topic id
for v in topic_dist.split('/'):
topic_values = v.split('-')
self.topic_enc_input.append(int(topic_values[0]))
self.topic_enc_weight.append(float(topic_values[1]))
self.topic_enc_len = len(
self.topic_enc_input) # store the length of topic encoder
# Get the decoder input sequence and target sequence
self.dec_input, self.target = self.get_dec_inp_targ_seqs(
abs_ids, hps.max_dec_steps, start_decoding, stop_decoding)
self.dec_len = len(self.dec_input)
# If using pointer-generator mode, we need to store some extra info
if hps.pointer_gen:
# Store a version of the enc_input where in-article OOVs are represented by their temporary OOV id; also store the in-article OOVs words themselves
self.enc_input_extend_vocab, self.article_oovs = data.article2ids(
article_words, vocab)
# Get a verison of the reference summary where in-article OOVs are represented by their temporary article OOV id
abs_ids_extend_vocab = data.abstract2ids(abstract_words, vocab,
self.article_oovs)
# Overwrite decoder target sequence so it uses the temp article OOV ids
_, self.target = self.get_dec_inp_targ_seqs(
abs_ids_extend_vocab, hps.max_dec_steps, start_decoding,
stop_decoding)
# Store the original strings
self.original_article = article
self.original_abstract = abstract
self.original_abstract_sents = abstract_sentences
if FLAGS.topic_aware:
self.original_topic_dist = np.zeros(FLAGS.num_topic)
for v in topic_dist.split('/'):
topic_values = v.split('-')
topic_id = int(topic_values[0])
self.original_topic_dist[topic_id] = float(topic_values[1])
def __init__(self, article, abstract_sentences, vocab, hps):
"""Initializes the Example, performing tokenization and truncation to produce the encoder, decoder and target sequences, which are stored in self.
Args:
article: source text; a string. each token is separated by a single space.
abstract_sentences: list of strings, one per abstract sentence. In each sentence, each token is separated by a single space.
vocab: Vocabulary object
hps: hyperparameters
"""
self.hps = hps
# Get ids of special tokens
start_decoding = vocab.word2id(data.START_DECODING)
stop_decoding = vocab.word2id(data.STOP_DECODING)
# Process the article
article_words = article.split()
# print ("Example __init__ article_words: ", len(article_words))# list of str
# print ("__init__ hps.max_enc_steps: ", hps.max_enc_steps) # train flag
if len(article_words) > hps.max_enc_steps.value:
article_words = article_words[:hps.max_enc_steps.value]
self.enc_len = len(
article_words
) # store the length after truncation but before padding
self.enc_input = [
vocab.word2id(w) for w in article_words
] # list of word ids; OOVs are represented by the id for UNK token
# print ("Example __init__ self.enc_len: ", self.enc_len) # int
# print ("Example __init__ self.enc_input: ", len(self.enc_input)) # list of int
# Process the abstract
# print ("abstract_sentences: ", abstract_sentences)
if hps.use_doc_vec.value:
abstract_sentences_list = abstract_sentences[0].split()
subred_tag = abstract_sentences_list[0]
# print ("subred_tag: ", subred_tag)
abstract_sentences = [' '.join(abstract_sentences_list[1:])]
# print ("abstract_sentences: ", abstract_sentences)
abstract = ' '.join(abstract_sentences) # string
abstract_words = abstract.split() # list of strings
abs_ids = [
vocab.word2id(w) for w in abstract_words
] # list of word ids; OOVs are represented by the id for UNK token
# Get the decoder input sequence and target sequence
self.dec_input, self.target = self.get_dec_inp_targ_seqs(
abs_ids, hps.max_dec_steps.value, start_decoding, stop_decoding)
self.dec_len = len(self.dec_input)
# If using pointer-generator mode, we need to store some extra info
# print("__init__ hps.pointer_gen: ", hps.pointer_gen) # train flag
if hps.pointer_gen.value:
# Store a version of the enc_input where in-article OOVs are represented by their temporary OOV id; also store the in-article OOVs words themselves
self.enc_input_extend_vocab, self.article_oovs = data.article2ids(
article_words, vocab)
# Get a verison of the reference summary where in-article OOVs are represented by their temporary article OOV id
abs_ids_extend_vocab = data.abstract2ids(abstract_words, vocab,
self.article_oovs)
# Overwrite decoder target sequence so it uses the temp article OOV ids
# print ("__init__ hps.max_dec_steps: ", hps.max_dec_steps) # train flag
_, self.target = self.get_dec_inp_targ_seqs(
abs_ids_extend_vocab, hps.max_dec_steps.value, start_decoding,
stop_decoding)
# Store the original strings
if hps.use_doc_vec.value:
self.subred_tag = SUBRED_TABLE[subred_tag]
self.original_article = article
self.original_abstract = abstract
self.original_abstract_sents = abstract_sentences
def __init__(self, article, abstract_sentences, vocab, hps):
"""Initializes the Example, performing tokenization and truncation to produce the encoder, decoder and target sequences, which are stored in self.
# example初始化，一个example是一个article-abstract对
Args:
article: source text; a string. each token is separated by a single space. （src text，用string表示）
abstract_sentences: list of strings, one per abstract sentence. In each sentence, each token is separated by a single space.（abstract sentences列表，用string表示）
vocab: Vocabulary object
hps: hyperparameters
"""
self.hps = hps
# Get ids of special tokens
# 得到start<s>和stop</s>的id
start_decoding = vocab.word2id(data.START_DECODING)
stop_decoding = vocab.word2id(data.STOP_DECODING)
# Process the article
# article string数组
article_words = article.split()
# 如果超过则裁剪
if len(article_words) > hps.max_enc_steps:
article_words = article_words[:hps.max_enc_steps]
# padding之前的长度
self.enc_len = len(
article_words
) # store the length after truncation but before padding
# article word ids，oov表示为unk
self.enc_input = [
vocab.word2id(w) for w in article_words
] # list of word ids; OOVs are represented by the id for UNK token
# Process the abstract
abstract = ' '.join(abstract_sentences) # string
abstract_words = abstract.split() # list of strings
# abstract word ids，oov表示为unk
abs_ids = [
vocab.word2id(w) for w in abstract_words
] # list of word ids; OOVs are represented by the id for UNK token
# Get the decoder input sequence and target sequence
# decoder的input是<s> + seq，target是seq + </s>
self.dec_input, self.target = self.get_dec_inp_targ_seqs(
abs_ids, hps.max_dec_steps, start_decoding, stop_decoding)
# dec input的长度（padding前）
self.dec_len = len(self.dec_input)
# If using pointer-generator mode, we need to store some extra info
if hps.pointer_gen:
# Store a version of the enc_input where in-article OOVs are represented by their temporary OOV id; also store the in-article OOVs words themselves
#如果定义了pointer gen，则得到扩展vocab（id list）和article中的oov（str list）
self.enc_input_extend_vocab, self.article_oovs = data.article2ids(
article_words, vocab)
# Get a verison of the reference summary where in-article OOVs are represented by their temporary article OOV id
# 得到abstract的ids（extend版本）
abs_ids_extend_vocab = data.abstract2ids(abstract_words, vocab,
self.article_oovs)
# Overwrite decoder target sequence so it uses the temp article OOV ids
# 覆盖target
_, self.target = self.get_dec_inp_targ_seqs(
abs_ids_extend_vocab, hps.max_dec_steps, start_decoding,
stop_decoding)
# Store the original strings
self.original_article = article
self.original_abstract = abstract
self.original_abstract_sents = abstract_sentences
def __init__(self, article, abstract_sentences, vocab, hps):
"""Initializes the Example, performing tokenization and truncation to produce the encoder, decoder and target sequences, which are stored in self.
Args:
article: source text; a string. each token is separated by a single space.
abstract_sentences: list of strings, one per abstract sentence. In each sentence, each token is separated by a single space.
vocab: Vocabulary object
hps: hyperparameters
"""
self.hps = hps
# Get ids of special tokens
start_decoding = vocab.word2id(data.START_DECODING)
stop_decoding = vocab.word2id(data.STOP_DECODING)
# Process the article
# Need to shuffle only the sentences within the hps.max_enc_steps.
original_article_clean = article
sentences = article.split('\n')
if (hps.keep_stopwords < 1.0) or (hps.keep_word < 1.0):
for i, sent in enumerate(sentences):
sent_processed = []
for word in sent.split(' '):
# Remove stopwords with specified probability
if hps.keep_stopwords < 1.0:
if (word.lower() in stopwords) and (
random.random() > hps.keep_stopwords):
continue
# Remove any word with specified probability.
if hps.keep_word < 1.0:
if (random.random() > hps.keep_word):
continue
sent_processed.append(word)
sentences[i] = ' '.join(sent_processed)
article = '\n'.join(sentences)
if hps.shuffle_sentences:
sentences = article.split('\n')
token_counter = 0
for idx, sent in enumerate(sentences):
token_counter += len(sent.split())
if token_counter >= hps.max_enc_steps:
sentences[idx] = ' '.join(sent.split()[:hps.max_enc_steps -
token_counter])
break
sentences = sentences[:idx + 1]
sentences = [
sent for sent in sentences if (sent != '\n' and sent != '')
]
random.shuffle(sentences)
article_words = ' '.join(sentences).split()
else:
article_words = article.split()
if len(article_words) > hps.max_enc_steps:
article_words = article_words[:hps.max_enc_steps]
self.enc_len = len(
article_words
) # store the length after truncation but before padding
self.enc_input = [
vocab.word2id(w) for w in article_words
] # list of word ids; OOVs are represented by the id for UNK token
# Process the abstract
abstract = ' '.join(abstract_sentences) # string
abstract_words = abstract.split() # list of strings
abs_ids = [
vocab.word2id(w) for w in abstract_words
] # list of word ids; OOVs are represented by the id for UNK token
# Get the decoder input sequence and target sequence
self.dec_input, self.target = self.get_dec_inp_targ_seqs(
abs_ids, hps.max_dec_steps, start_decoding, stop_decoding)
self.dec_len = len(self.dec_input)
# If using pointer-generator mode, we need to store some extra info
if hps.pointer_gen:
# Store a version of the enc_input where in-article OOVs are represented by their temporary OOV id; also store the in-article OOVs words themselves
self.enc_input_extend_vocab, self.article_oovs = data.article2ids(
article_words, vocab)
# Get a verison of the reference summary where in-article OOVs are represented by their temporary article OOV id
abs_ids_extend_vocab = data.abstract2ids(abstract_words, vocab,
self.article_oovs)
# Overwrite decoder target sequence so it uses the temp article OOV ids
_, self.target = self.get_dec_inp_targ_seqs(
abs_ids_extend_vocab, hps.max_dec_steps, start_decoding,
stop_decoding)
# Store the original strings
self.original_article_clean = original_article_clean
self.original_article = article
self.original_abstract = abstract
self.original_abstract_sents = abstract_sentences
def __init__(self, article, abstract_sentences, vocab, hps):
"""Initializes the Example, performing tokenization and truncation to produce the encoder, decoder and target sequences, which are stored in self.
Args:
article: source text; a string. each token is separated by a single space.
abstract_sentences: list of strings, one per abstract sentence. In each sentence, each token is separated by a single space.
vocab: Vocabulary object
hps: hyperparameters
"""
self.hps = hps
# Get ids of special tokens
start_decoding = vocab.word2id(data.START_DECODING)
stop_decoding = vocab.word2id(data.STOP_DECODING)
# Process the article
# process sent by viet nguyen
sent_text = nltk.sent_tokenize(article)
list_number_sent=[]
article_words = []
for sentence in sent_text:
split_sent = sentence.split()
try:
number_sent = int((split_sent[0][1:-1]))
except ValueError:
number_sent = 100
s = split_sent[1:]
len_sent = len(s)
for id in range(len_sent):
list_number_sent.append(number_sent)
article_words.append(s[id])
# article_words = article.split()
# caculate tf
word_dict = set(article_words)
wordDictA = dict.fromkeys(word_dict, 0)
for sentence in sent_text:
split_sent = sentence.split()
try:
number_sent = int((split_sent[0][1:-1]))
except ValueError:
number_sent = 100
s = split_sent[1:]
for word in s:
wordDictA[word]+=1
tf_dict = {}
tf_list=[]
for word, count in wordDictA.items():
tf_dict[word] = count/float(len(article_words))
tf_list.append(tf_dict[word])
if len(tf_list) > hps.max_enc_steps:
tf_list = tf_list[:hps.max_enc_steps]
if len(list_number_sent) > hps.max_enc_steps:
list_number_sent = list_number_sent[:hps.max_enc_steps]
if len(article_words) > hps.max_enc_steps:
article_words = article_words[:hps.max_enc_steps]
self.enc_tf_list = tf_list
self.enc_number_sent = list_number_sent
self.enc_len = len(article_words) # store the length after truncation but before padding
self.enc_input = [vocab.word2id(w) for w in article_words] # list of word ids; OOVs are represented by the id for UNK token
# Process the abstract
abstract = ' '.join(abstract_sentences) # string
abstract_words = abstract.split() # list of strings
abs_ids = [vocab.word2id(w) for w in abstract_words] # list of word ids; OOVs are represented by the id for UNK token
# Get the decoder input sequence and target sequence
self.dec_input, self.target = self.get_dec_inp_targ_seqs(abs_ids, hps.max_dec_steps, start_decoding, stop_decoding)
self.dec_len = len(self.dec_input)
# If using pointer-generator mode, we need to store some extra info
if hps.pointer_gen:
# Store a version of the enc_input where in-article OOVs are represented by their temporary OOV id; also store the in-article OOVs words themselves
self.enc_input_extend_vocab, self.article_oovs = data.article2ids(article_words, vocab)
# Get a verison of the reference summary where in-article OOVs are represented by their temporary article OOV id
abs_ids_extend_vocab = data.abstract2ids(abstract_words, vocab, self.article_oovs)
# Overwrite decoder target sequence so it uses the temp article OOV ids
_, self.target = self.get_dec_inp_targ_seqs(abs_ids_extend_vocab, hps.max_dec_steps, start_decoding, stop_decoding)
# Store the original strings
self.original_article = article
self.original_abstract = abstract
self.original_abstract_sents = abstract_sentences
def __init__(self, article, abstract_sentences, vocab, hps, log_path):
"""Initializes the Example, performing tokenization and truncation to produce the encoder, decoder and target sequences, which are stored in self.
Args:
article: source text; a string. each token is separated by a single space.
abstract_sentences: list of strings, one per abstract sentence. In each sentence, each token is separated by a single space.
vocab: Vocabulary object
hps: hyperparameters
"""
self.hps = hps
# Get ids of special tokens
start_decoding = vocab.word2id(data.START_DECODING)
stop_decoding = vocab.word2id(data.STOP_DECODING)
# Process the article
article_words = article.split()
if len(article_words) > hps.max_enc_steps:
article_words = article_words[:hps.max_enc_steps]
self.enc_len = len(
article_words
) # store the length after truncation but before padding
self.enc_input = [
vocab.word2id(w) for w in article_words
] # list of word ids; OOVs are represented by the id for UNK token
if log_path is not None:
# For testing
reference_cluster_dir = os.path.join(log_path, "reference")
if not os.path.exists(reference_cluster_dir):
os.makedirs(reference_cluster_dir)
# Process the abstract
abstract = ' '.join(abstract_sentences) # string
abstract = run_coreference_resolution_for_testing(
abstract, reference_cluster_dir)
abstract_words = abstract.split() # list of strings
abs_ids = [
vocab.word2id(w) for w in abstract_words
] # list of word ids; OOVs are represented by the id for UNK token
else:
# Process the abstract
abstract = ' '.join(abstract_sentences) # string
abstract = run_coreference_resolution_for_training(abstract)
abstract_words = abstract.split() # list of strings
abs_ids = [
vocab.word2id(w) for w in abstract_words
] # list of word ids; OOVs are represented by the id for UNK token
# Get the decoder input sequence and target sequence
self.dec_input, self.target = self.get_dec_inp_targ_seqs(
abs_ids, hps.max_dec_steps, start_decoding, stop_decoding)
self.dec_len = len(self.dec_input)
# If using pointer-generator mode, we need to store some extra info
if hps.pointer_gen:
# Store a version of the enc_input where in-article OOVs are represented by their temporary OOV id; also store the in-article OOVs words themselves
self.enc_input_extend_vocab, self.article_oovs = data.article2ids(
article_words, vocab)
# Get a verison of the reference summary where in-article OOVs are represented by their temporary article OOV id
abs_ids_extend_vocab = data.abstract2ids(abstract_words, vocab,
self.article_oovs)
if hps.coreference_resolution:
indices = []
antecedent = get_antecedent(abstract)
if antecedent != None:
for key in antecedent:
positions = [
i for i, j in enumerate(abstract_words) if j == key
]
if positions:
positions = np.asarray(positions)
closest_index = positions[(
np.abs(positions - antecedent[key])).argmin()]
indices.append(closest_index)
idx = 0
if indices:
for i in range(len(abs_ids_extend_vocab)):
if i == indices[idx]:
if idx < len(indices) - 1:
idx += 1
else:
continue
else:
abs_ids_extend_vocab[i] = 0
else:
# Overwrite decoder target sequence so it uses the temp article OOV ids
_, self.target = self.get_dec_inp_targ_seqs(
abs_ids_extend_vocab, hps.max_dec_steps,
start_decoding, stop_decoding)
# Store the original strings
self.original_article = article
self.original_abstract = abstract
self.original_abstract_sents = abstract_sentences
def __init__(self, article, abstract_sentences, vocab, hps):
"""Initializes the Example, performing tokenization and truncation to produce the encoder, decoder and target sequences, which are stored in self.
Args:
article: source text; a string. each token is separated by a single space.
abstract_sentences: list of strings, one per abstract sentence. In each sentence, each token is separated by a single space.
vocab: Vocabulary object
hps: hyperparameters
"""
#Leena change article and abstract_sentences here if you want to do a quick decoding test
article = article.lower()
self.hps = hps
# Get ids of special tokens
start_decoding = vocab.word2id(data.START_DECODING)
stop_decoding = vocab.word2id(data.STOP_DECODING)
# Process the article
article_words = article.split()
#Leena: Trying to uderstand the data pipeline
# print("article_words: %s"%article_words) #word tokens; this would be used further in the code
if len(article_words) > hps.max_enc_steps:
article_words = article_words[:hps.max_enc_steps]
self.enc_len = len(
article_words
) # store the length after truncation but before padding
self.enc_input = [
vocab.word2id(w) for w in article_words
] # list of word ids; OOVs are represented by the id for UNK token
# Process the abstract
abstract = ' '.join(abstract_sentences) # string
#Leena: used this to save summariies in a file to train the LM
# if hps.mode == 'train':
# with open("/home/leena/Documents/thesis/pointer-gen/pointer-generator-master/nlm/train_data.txt", "a") as myfile:
# myfile.write("\n%s\n"%abstract)
# if hps.mode == 'eval':
# with open("/home/leena/Documents/thesis/pointer-gen/pointer-generator-master/nlm/valid_data.txt", "a") as myfile:
# myfile.write("\n%s\n"%abstract)
#
# if hps.mode == 'decode':
# with open("/home/leena/Documents/thesis/pointer-gen/pointer-generator-master/nlm/test_data.txt", "a") as myfile:
# myfile.write("\n%s\n"%abstract)
abstract_words = abstract.split() # list of strings
abs_ids = [
vocab.word2id(w) for w in abstract_words
] # list of word ids; OOVs are represented by the id for UNK token
# Get the decoder input sequence and target sequence
self.dec_input, self.target = self.get_dec_inp_targ_seqs(
abs_ids, hps.max_dec_steps, start_decoding, stop_decoding)
self.dec_len = len(self.dec_input)
# If using pointer-generator mode, we need to store some extra info
if hps.pointer_gen:
# Store a version of the enc_input where in-article OOVs are represented by their temporary OOV id; also store the in-article OOVs words themselves
self.enc_input_extend_vocab, self.article_oovs = data.article2ids(
article_words, vocab)
# Get a verison of the reference summary where in-article OOVs are represented by their temporary article OOV id
abs_ids_extend_vocab = data.abstract2ids(abstract_words, vocab,
self.article_oovs)
# Overwrite decoder target sequence so it uses the temp article OOV ids
_, self.target = self.get_dec_inp_targ_seqs(
abs_ids_extend_vocab, hps.max_dec_steps, start_decoding,
stop_decoding)
# Store the original strings
self.original_article = article
self.original_abstract = abstract
self.original_abstract_sents = abstract_sentences
def __init__(self, article, abstract_sentences, categoryWord, tagWord,
vocab, hps):
"""Initializes the Example, performing tokenization and truncation to produce the encoder, decoder and target sequences, which are stored in self.
Args:
article: source text; a string. each token is separated by a single space.
abstract_sentences: list of strings, one per abstract sentence. In each sentence, each token is separated by a single space.
vocab: Vocabulary object
hps: hyperparameters
"""
self.hps = hps
# Get ids of special tokens
start_decoding = vocab.word2id(data.START_DECODING)
stop_decoding = vocab.word2id(data.STOP_DECODING)
# Process the article
article_words = article.split()
if len(article_words) > hps.max_enc_steps:
article_words = article_words[:hps.max_enc_steps]
self.enc_len = len(
article_words
) # store the length after truncation but before padding
# list of word ids; OOVs are represented by the id for UNK token
self.enc_input = [vocab.word2id(w) for w in article_words]
# Process the abstract
abstract = ' '.join(abstract_sentences) # string
abstract_words = abstract.split() # list of strings
# list of word ids; OOVs are represented by the id for UNK token
abs_ids = [vocab.word2id(w) for w in abstract_words]
self.category_id = vocab.word2id(categoryWord)
tagWords = jieba.lcut(tagWord)
self.tag_ids = list()
for i in tagWords:
self.tag_ids.append(vocab.word2id(i))
# categoryWords = list()
# for word in set(article_words):
# if word in vocab._word_to_id.keys():
# categoryWords.append((word, vocab.embedding_model.similarity(categoryWord,word)))
# categoryWords.sort(key=lambda x: x[1],reverse=True)
# Get the decoder input sequence and target sequence
self.dec_input, self.target = self.get_dec_inp_targ_seqs(
abs_ids, hps.max_dec_steps, start_decoding, stop_decoding)
self.dec_len = len(self.dec_input)
# If using pointer-generator mode, we need to store some extra info
if hps.pointer_gen:
# Store a version of the enc_input where in-article OOVs are represented by their temporary OOV id; also store the in-article OOVs words themselves
self.enc_input_extend_vocab, self.article_oovs = data.article2ids(
article_words, vocab)
# Get a verison of the reference summary where in-article OOVs are represented by their temporary article OOV id
abs_ids_extend_vocab = data.abstract2ids(abstract_words, vocab,
self.article_oovs)
# Overwrite decoder target sequence so it uses the temp article OOV ids
_, self.target = self.get_dec_inp_targ_seqs(
abs_ids_extend_vocab, hps.max_dec_steps, start_decoding,
stop_decoding)
# Store the original strings
self.original_article = article
self.original_abstract = abstract
self.original_abstract_sents = abstract_sentences
self.original_categoryWord = categoryWord
self.original_tagWords = tagWords
