def preprocess_document(documents):
preprocessed_documents = []
preprocessed_documents_tokens = []
for document in documents:
re_document = build_source_file.reform_text(document)
re_token, re_document = utility_function.tokenize(re_document,
rm_stop=True)
preprocessed_documents.append(re_document)
preprocessed_documents_tokens.append(re_token)
preprocessed_documents_tokens = multsum_preprocess.preprocess(
preprocessed_documents_tokens)
return preprocessed_documents, preprocessed_documents_tokens
def summarize_documents(documents, stopwordsFilename=DEFAULT_STOPWORDS, length=DEFAULT_SUMMARY_LENGTH, unit=UNIT_WORDS, use_tfidf_similarity=True, use_sentiment_similarity=True, use_w2v_similarity=True, w2v_vector_file=W2V_VECTOR_FILE, preloaded_w2v_wordmodel=None, w2v_backend=False, w2v_experiments="", quiet=False, output_numbers=False, use_aggregate_for_clustering=False, anaphora_resolution_simple=False, min_sentence_length=MIN_SENTENCE_LENGTH):
# print 'summarize_documents()'
#for l in documents:
# for s in l:
# print s
documents = multsum_preprocess.preprocess(documents, anaphora_resolution_simple=anaphora_resolution_simple, quiet=quiet)
sentsims = get_def_sentsims(documents, stopwordsFilename, None)
matrices = list()
flat_sentences = [sentence for document in documents for sentence in document]
#for sentence in flat_sentences:
# print sentence
if use_sentiment_similarity:
(pos, neg) = analyze_sentiment(flat_sentences)
matrices.append(pos)
matrices.append(neg)
if use_w2v_similarity:
if not quiet:
print('Computing sentence similarities based on word2vec.')
wordmodel = None
if preloaded_w2v_wordmodel:
wordmodel = preloaded_w2v_wordmodel
elif not w2v_backend:
wordmodel = load_w2v_wordmodel(w2v_vector_file)
if wordmodel or w2v_backend:
w2v_matrix = get_w2v_matrix(flat_sentences, wordmodel, w2v_backend, get_stopwords(stopwordsFilename), documents, w2v_experiments, quiet=quiet)
if not w2v_matrix is None:
matrices.append(w2v_matrix)
if use_tfidf_similarity or len(matrices) == 0:
# this is also used for fallback if the others were specified and failed for some reason.
matrices.append(sentsims["tfidf_cosine"])
if not quiet:
print 'Input sentences:'
for l in documents:
for s in l:
print ' '+' '.join(s)
#for m in matrices:
# for i in range(0,m.shape[0]):
# for j in range(0,m.shape[1]):
# print str(m[i][j])+' ',
# print 'EOL'
summary_set = select_sentences(length,
matrices,
sentsims["idf_vectors"],
documents,
unit,
None,
'summarization_doc',
use_aggregate_for_clustering=use_aggregate_for_clustering,
clustering_matrix=None)
summary_list = list(summary_set)
summary_list.sort()
return_string = ''
#if not quiet:
# print 'Summary:'
for i in summary_list:
if output_numbers:
#print "outputting numbers: %d"%(i+1)
return_string += "%d\n"%(i+1)
else:
return_string += ' '.join(get_sentence_index(i, documents))+'\n'
#if not quiet:
# print(' '+get_sentence_index(i, documents))
return return_string
def summarize_strings(sentences_lists,
stopwordsFilename=DEFAULT_STOPWORDS,
length=DEFAULT_SUMMARY_LENGTH,
unit=UNIT_WORDS,
use_tfidf_similarity=True,
use_sentiment_similarity=True,
use_w2v_similarity=True,
w2v_vector_file=W2V_VECTOR_FILE,
split_sentences=False,
preloaded_w2v_wordmodel=None,
w2v_backend=False,
w2v_experiments="",
quiet=False,
output_numbers=False,
use_aggregate_for_clustering=False,
anaphora_resolution_simple=False,
min_sentence_length=MIN_SENTENCE_LENGTH):
# print 'summarize_strings()'
#for l in sentences_lists:
# for s in l:
# print s
sentence_count = 0
if split_sentences:
if not quiet:
print 'splitting'
splittedLists = []
for l in sentences_lists:
splittedList = []
documentstring = ""
for s in l:
documentstring += " \n" + s
#splitted = re.split('[\.!?]', s)
splitted = re.split('(?<=[\.!\?])\W+', documentstring)
for s in splitted:
stripped = s.strip()
if stripped and stripped.count(' ') + 1 > min_sentence_length:
sentence_count += 1
splittedList.append(s.replace('\n', ' '))
#print s
splittedLists.append(splittedList)
sentences_lists = splittedLists
elif min_sentence_length > 0:
new_sentences_lists = list()
for l in sentences_lists:
new_l = list()
for s in l:
if s.strip().count(' ') + 1 <= min_sentence_length:
sentence_count += 1
new_l.append(s)
new_sentences_lists.append(new_l)
sentences_lists = new_sentences_lists
if not quiet:
print "Total sentence count after min length filtering and (possibly) splitting: %d" % (
sentence_count)
sentences_lists = multsum_preprocess.preprocess(
sentences_lists, anaphora_resolution_simple=anaphora_resolution_simple)
sentsims = get_def_sentsims(sentences_lists, stopwordsFilename, None)
matrices = list()
flat_sentences = [
sentence for document in sentences_lists for sentence in document
]
#for sentence in flat_sentences:
# print sentence
if use_sentiment_similarity:
(pos, neg) = analyze_sentiment(flat_sentences)
matrices.append(pos)
matrices.append(neg)
if use_w2v_similarity:
if not quiet:
print('Computing sentence similarities based on word2vec.')
wordmodel = None
if preloaded_w2v_wordmodel:
wordmodel = preloaded_w2v_wordmodel
elif not w2v_backend:
wordmodel = load_w2v_wordmodel(w2v_vector_file)
if wordmodel or w2v_backend:
w2v_matrix = get_w2v_matrix(flat_sentences,
wordmodel,
w2v_backend,
get_stopwords(stopwordsFilename),
sentences_lists,
w2v_experiments,
quiet=quiet)
if not w2v_matrix is None:
matrices.append(w2v_matrix)
if use_tfidf_similarity or len(matrices) == 0:
# this is also used for fallback if the others were specified and failed for some reason.
matrices.append(sentsims["tfidf_cosine"])
if not quiet:
print 'Input sentences:'
for l in sentences_lists:
for s in l:
print ' ' + s
#for m in matrices:
# for i in range(0,m.shape[0]):
# for j in range(0,m.shape[1]):
# print str(m[i][j])+' ',
# print 'EOL'
summary_set = select_sentences(
length,
matrices,
sentsims["idf_vectors"],
sentences_lists,
unit,
None,
'summarization_doc',
use_aggregate_for_clustering=use_aggregate_for_clustering)
summary_list = list(summary_set)
summary_list.sort()
return_string = ''
#if not quiet:
# print 'Summary:'
for i in summary_list:
if output_numbers:
#print "outputting numbers: %d"%(i+1)
return_string += "%d\n" % (i + 1)
else:
return_string += get_sentence_index(i, sentences_lists) + '\n'
#if not quiet:
# print(' '+get_sentence_index(i, sentences_lists))
return return_string
def summarize_documents(documents, stopwordsFilename=DEFAULT_STOPWORDS, length=DEFAULT_SUMMARY_LENGTH, unit=UNIT_WORDS, use_tfidf_similarity=True, use_sentiment_similarity=True, use_w2v_similarity=True, w2v_vector_file=W2V_VECTOR_FILE, preloaded_w2v_wordmodel=None, w2v_backend=False, w2v_experiments="", quiet=False, output_numbers=False, use_aggregate_for_clustering=False, anaphora_resolution_simple=False, min_sentence_length=MIN_SENTENCE_LENGTH):
# print 'summarize_documents()'
#for l in documents:
# for s in l:
# print s
documents = multsum_preprocess.preprocess(documents, anaphora_resolution_simple=anaphora_resolution_simple, quiet=quiet)
sentsims = get_def_sentsims(documents, stopwordsFilename, None)
matrices = list()
flat_sentences = [sentence for document in documents for sentence in document]
#for sentence in flat_sentences:
# print sentence
if use_sentiment_similarity:
(pos, neg) = analyze_sentiment(flat_sentences)
matrices.append(pos)
matrices.append(neg)
if use_w2v_similarity:
if not quiet:
print('Computing sentence similarities based on word2vec.')
wordmodel = None
if preloaded_w2v_wordmodel:
wordmodel = preloaded_w2v_wordmodel
elif not w2v_backend:
wordmodel = load_w2v_wordmodel(w2v_vector_file)
if wordmodel or w2v_backend:
w2v_matrix = get_w2v_matrix(flat_sentences, wordmodel, w2v_backend, get_stopwords(stopwordsFilename), documents, w2v_experiments, quiet=quiet)
if not w2v_matrix is None:
matrices.append(w2v_matrix)
if use_tfidf_similarity or len(matrices) == 0:
# this is also used for fallback if the others were specified and failed for some reason.
matrices.append(sentsims["tfidf_cosine"])
if not quiet:
print 'Input sentences:'
for l in documents:
for s in l:
print ' '+' '.join(s)
#for m in matrices:
# for i in range(0,m.shape[0]):
# for j in range(0,m.shape[1]):
# print str(m[i][j])+' ',
# print 'EOL'
summary_set = select_sentences(length,
matrices,
sentsims["idf_vectors"],
documents,
unit,
None,
'summarization_doc',
use_aggregate_for_clustering=use_aggregate_for_clustering)
summary_list = list(summary_set)
summary_list.sort()
return_string = ''
#if not quiet:
# print 'Summary:'
for i in summary_list:
if output_numbers:
#print "outputting numbers: %d"%(i+1)
return_string += "%d\n"%(i+1)
else:
return_string += ' '.join(get_sentence_index(i, documents))+'\n'
#if not quiet:
# print(' '+get_sentence_index(i, documents))
return return_string
def summarize_strings(sentences_lists, stopwordsFilename=DEFAULT_STOPWORDS, length=DEFAULT_SUMMARY_LENGTH, unit=UNIT_WORDS, use_tfidf_similarity=True, use_sentiment_similarity=True, use_w2v_similarity=True, w2v_vector_file=W2V_VECTOR_FILE, split_sentences=False, preloaded_w2v_wordmodel=None, w2v_backend=False, w2v_experiments="", quiet=False, output_numbers=False, use_aggregate_for_clustering=False, anaphora_resolution_simple=False, min_sentence_length=MIN_SENTENCE_LENGTH):
# print 'summarize_strings()'
#for l in sentences_lists:
# for s in l:
# print s
sentence_count = 0
if split_sentences:
if not quiet:
print 'splitting'
splittedLists = []
for l in sentences_lists:
splittedList = []
documentstring = ""
for s in l:
documentstring += " \n"+s
#splitted = re.split('[\.!?]', s)
splitted = re.split('(?<=[\.!\?])\W+', documentstring)
for s in splitted:
stripped = s.strip()
if stripped and stripped.count(' ')+1 > min_sentence_length:
sentence_count += 1
splittedList.append(s.replace('\n', ' '))
#print s
splittedLists.append(splittedList)
sentences_lists = splittedLists
elif min_sentence_length > 0:
new_sentences_lists = list()
for l in sentences_lists:
new_l = list()
for s in l:
if s.strip().count(' ')+1 <= min_sentence_length:
sentence_count += 1
new_l.append(s)
new_sentences_lists.append(new_l)
sentences_lists = new_sentences_lists
if not quiet:
print "Total sentence count after min length filtering and (possibly) splitting: %d"%(sentence_count)
sentences_lists = multsum_preprocess.preprocess(sentences_lists, anaphora_resolution_simple=anaphora_resolution_simple)
sentsims = get_def_sentsims(sentences_lists, stopwordsFilename, None)
matrices = list()
flat_sentences = [sentence for document in sentences_lists for sentence in document]
#for sentence in flat_sentences:
# print sentence
if use_sentiment_similarity:
(pos, neg) = analyze_sentiment(flat_sentences)
matrices.append(pos)
matrices.append(neg)
if use_w2v_similarity:
if not quiet:
print('Computing sentence similarities based on word2vec.')
wordmodel = None
if preloaded_w2v_wordmodel:
wordmodel = preloaded_w2v_wordmodel
elif not w2v_backend:
wordmodel = load_w2v_wordmodel(w2v_vector_file)
if wordmodel or w2v_backend:
w2v_matrix = get_w2v_matrix(flat_sentences, wordmodel, w2v_backend, get_stopwords(stopwordsFilename), sentences_lists, w2v_experiments, quiet=quiet)
if not w2v_matrix is None:
matrices.append(w2v_matrix)
if use_tfidf_similarity or len(matrices) == 0:
# this is also used for fallback if the others were specified and failed for some reason.
matrices.append(sentsims["tfidf_cosine"])
if not quiet:
print 'Input sentences:'
for l in sentences_lists:
for s in l:
print ' '+s
#for m in matrices:
# for i in range(0,m.shape[0]):
# for j in range(0,m.shape[1]):
# print str(m[i][j])+' ',
# print 'EOL'
summary_set = select_sentences(length,
matrices,
sentsims["idf_vectors"],
sentences_lists,
unit,
None,
'summarization_doc',
use_aggregate_for_clustering=use_aggregate_for_clustering)
summary_list = list(summary_set)
summary_list.sort()
return_string = ''
#if not quiet:
# print 'Summary:'
for i in summary_list:
if output_numbers:
#print "outputting numbers: %d"%(i+1)
return_string += "%d\n"%(i+1)
else:
return_string += get_sentence_index(i, sentences_lists)+'\n'
#if not quiet:
# print(' '+get_sentence_index(i, sentences_lists))
return return_string
