def text_rank(json_request):
pattern = re.compile("TI - (.*?)\\r|AB - (.*?)\\r")
matches = re.findall(pattern, json_request['ris'])
all_inputs = []
for section in matches:
all_inputs.append((''.join([word + ' ' for word in section])).strip())
input_json = {}
input_json['id'] = "0"
input_json['text'] = '.'.join(all_inputs)
with open('ris_extracted.json', 'w') as output:
json.dump(input_json, output)
with open(path_stage1, 'w') as f:
for graf in pytextrank.parse_doc(pytextrank.json_iter('ris_extracted.json')):
f.write("%s\n" % pytextrank.pretty_print(graf._asdict()))
graph, ranks = pytextrank.text_rank(path_stage1)
with open(path_stage2, 'w') as f:
for rl in pytextrank.normalize_key_phrases(path_stage1, ranks):
f.write("%s\n" % pytextrank.pretty_print(rl._asdict()))
phrases = list([p for p in pytextrank.limit_keyphrases(path_stage2, phrase_limit=20)])
return phrases
def summarize(self, _id, content_text, word_limit):
self.logger.log("_id: " + _id)
self.logger.log("word_limit: " + str(word_limit))
# File names
path_stage0 = 'process/' + _id + '.json'
path_stage1 = 'process/' + _id + '_o1.json'
path_stage2 = 'process/' + _id + '_o2.json'
path_stage3 = 'process/' + _id + '_o3.json'
path_stage4 = 'process/' + _id + '_o4.json'
# Create input file
with open(path_stage0, 'w') as outfile:
json.dump({"id": "123", "text": content_text}, outfile)
# Statistical Parsing - Stage 1
# Perform statistical parsing/tagging on a document in JSON format
with open(path_stage1, 'w') as f:
for graf in pytextrank.parse_doc(
pytextrank.json_iter(path_stage0)):
f.write("%s\n" % pytextrank.pretty_print(graf._asdict()))
# Ranked Keyphrases - Stage 2
# Collect and normalize the key phrases from a parsed document
graph, ranks = pytextrank.text_rank(path_stage1)
pytextrank.render_ranks(graph, ranks)
with open(path_stage2, 'w') as f:
for rl in pytextrank.normalize_key_phrases(path_stage1, ranks):
f.write("%s\n" % pytextrank.pretty_print(rl._asdict()))
# Extractive Summarization - Stage 3
# Calculate a significance weight for each sentence, using MinHash to approximate a Jaccard distance from key phrases determined by TextRank
kernel = pytextrank.rank_kernel(path_stage2)
with open(path_stage3, 'w') as f:
for s in pytextrank.top_sentences(kernel, path_stage1):
f.write(pytextrank.pretty_print(s._asdict()))
f.write("\n")
# Final Output - Stage 4
# Summarize a document based on most significant sentences and key phrases
phrases = ", ".join(
set([
p for p in pytextrank.limit_keyphrases(path_stage2,
phrase_limit=12)
]))
sent_iter = sorted(pytextrank.limit_sentences(path_stage3,
word_limit=word_limit),
key=lambda x: x[1])
s = []
for sent_text, idx in sent_iter:
s.append(pytextrank.make_sentence(sent_text))
graf_text = " ".join(s)
return {'excerpts': graf_text, 'keywords': phrases}
def stage_2():
cur_dir = os.path.dirname(__file__)
data_dir = stage_1_dir
ids = os.listdir(data_dir)
result_dir = stage_2_dir
if os.path.exists(result_dir):
shutil.rmtree(result_dir, ignore_errors=True)
os.mkdir(result_dir)
os.chdir(result_dir)
if not os.path.exists('pictures'):
os.mkdir('pictures')
for cur_id in ids:
if os.path.exists(cur_id):
continue
cur_file_name = data_dir + "\\" + cur_id
print(cur_id)
graph, ranks = pytextrank.text_rank(cur_file_name)
pytextrank.render_ranks(graph, ranks, cur_id)
with codecs.open(cur_id, "w+", "utf_8_sig") as file:
for rl in pytextrank.normalize_key_phrases(cur_file_name, ranks):
file.write("%s\n" % pytextrank.pretty_print(rl._asdict()))
os.chdir(cur_dir)
def summarize_text(input_file):
# seriously f**k this API
path_stage0 = input_file
path_stage1 = 'stage1.txt'
with open(path_stage1, 'w') as f:
for graf in pytextrank.parse_doc(pytextrank.json_iter(path_stage0)):
f.write("%s\n" % pytextrank.pretty_print(graf._asdict()))
# to view output in this notebook
#print(pytextrank.pretty_print(graf))
graph, ranks = pytextrank.text_rank(path_stage1)
pytextrank.render_ranks(graph, ranks)
path_stage2 = 'stage2.txt'
with open(path_stage2, 'w') as f:
for rl in pytextrank.normalize_key_phrases(path_stage1, ranks):
f.write("%s\n" % pytextrank.pretty_print(rl._asdict()))
# to view output in this notebook
#print(pytextrank.pretty_print(rl))
path_stage3 = 'stage3.txt'
kernel = pytextrank.rank_kernel(path_stage2)
with open(path_stage3, 'w') as f:
for s in pytextrank.top_sentences(kernel, path_stage1):
f.write(pytextrank.pretty_print(s._asdict()))
f.write("\n")
# to view output in this notebook
#print(pytextrank.pretty_print(s._asdict()))
phrases = ", ".join(
set([
p
for p in pytextrank.limit_keyphrases(path_stage2, phrase_limit=12)
]))
sent_iter = sorted(pytextrank.limit_sentences(path_stage3, word_limit=120),
key=lambda x: x[1])
s = []
for sent_text, idx in sent_iter:
s.append(pytextrank.make_sentence(sent_text))
graf_text = " ".join(s)
#print("**excerpts:** %s\n\n**keywords:** %s" % (graf_text, phrases,))
return ' '.join(s)
def perform_statistical_parsing_tagging(self, text_file, paragraph_output):
"""
Perform statistical parsing and tagging of
sentences in the text (aka JSON document)
"""
with open(paragraph_output, 'w') as temp_file:
for paragraph in pytextrank.parse_doc(pytextrank.json_iter(text_file)):
temp_file.write("%s\n" % pytextrank.pretty_print(paragraph._asdict()))
def RankedGraph(self, parse_list):
graph, ranks = xang.text_rank(parse_list)
norm_rank=xang.normalize_key_phrases(parse_list, ranks, self.stopwords, self.spacy_nlp, self.skip_ner)
norm_rank_list=[json.loads(pretty_print(rl._asdict())) for rl in norm_rank ]
phrases = ", ".join(set([p for p in xang.limit_keyphrases(norm_rank_list, self.phrase_limit)]))
# return a matrix like result for the top keywords
kernel = xang.rank_kernel(norm_rank_list)
self.phrases = phrases
self.kernel = kernel
return kernel
def collect_and_normalise_key_phrases(self, paragraph_output, key_phrases_output):
"""
Collect and normalise key phrases from the sentences in
the paragraph (in the JSON doc)
Rank them using PyTextRank, return a graph and ranked tokens
"""
graph, token_ranks = pytextrank.text_rank(paragraph_output)
pytextrank.render_ranks(graph, token_ranks)
with open(key_phrases_output, 'w') as temp_file:
for relationship in pytextrank.normalize_key_phrases(paragraph_output, token_ranks):
temp_file.write("%s\n" % pytextrank.pretty_print(relationship._asdict()))
return graph, token_ranks
def calculate_sentence_significance(self, paragraph_output, key_phrases_output, \
top_sentences_output, top_n_sentences):
"""
Calculate the significance of each sentence based on the ranking.
Ranking is determined by the top n sentences (filter)
"""
kernel = pytextrank.rank_kernel(key_phrases_output)
with open(top_sentences_output, 'w') as temp_file:
counter = 0
for sentence in pytextrank.top_sentences(kernel, paragraph_output):
if counter < top_n_sentences:
temp_file.write(pytextrank.pretty_print(sentence._asdict()))
temp_file.write("\n")
else:
return
counter = counter + 1
def stage_3():
cur_dir = os.path.dirname(__file__)
data_dir = stage_1_dir
ids = os.listdir(data_dir)
result_dir = stage_3_dir
if os.path.exists(result_dir):
shutil.rmtree(result_dir, ignore_errors=True)
os.mkdir(result_dir)
os.chdir(result_dir)
for cur_id in ids:
print(cur_id)
kernel = pytextrank.rank_kernel(stage_2_dir + '\\' + cur_id)
with codecs.open(cur_id, "w+", "utf_8_sig") as file:
for s in pytextrank.top_sentences(kernel,
stage_1_dir + '\\' + cur_id):
file.write(pytextrank.pretty_print(s._asdict()))
file.write("\n")
os.chdir(cur_dir)
def _get_keywords(path_stage0, path_stage2):
# Stage 1: parse doc
path_stage1 = 'o1.json'
with open(path_stage1, 'w') as f:
for graf in pytextrank.parse_doc(pytextrank.json_iter(path_stage0)):
f.write("%s\n" % pytextrank.pretty_print(graf._asdict()))
# Stage 2: rank words
graph, ranks = pytextrank.text_rank(path_stage1)
pytextrank.render_ranks(graph, ranks)
result_dict = dict()
with open(path_stage2, 'w') as f2:
for rl in pytextrank.normalize_key_phrases(path_stage1, ranks):
_ro = rl._asdict()
ro = dict()
ro[_ro['text']] = _ro['rank']
#f2.write("%s\n" % pytextrank.pretty_print(ro))
result_dict[_ro['text']] = _ro['rank']
return result_dict
def stage_1():
cur_dir = os.path.dirname(__file__)
data_dir = os.path.join(cur_dir, "..\cnn")
db = StoriesCollection(data_dir)
result_dir = stage_1_dir
if os.path.exists(result_dir):
shutil.rmtree(result_dir, ignore_errors=True)
os.mkdir(result_dir)
os.chdir(result_dir)
while not db.was_cycle():
cur_corpus = db.get_next_corpus_textrank()
cur_doc_name = db.get_cur_doc_name()
print(cur_doc_name)
with codecs.open(cur_doc_name[:-6] + '.json', "w+",
"utf_8_sig") as file:
for graf in parse_next_doc(cur_corpus, db.get_cur_id()):
file.write("%s\n" % pytextrank.pretty_print(graf._asdict()))
os.chdir(cur_dir)
