def make_tag(data):
stopwords = {'기자'}
#전처리
text = preprocessing(data)
#문장별로 나눠줌
texts = text.split('.')
tag = ''
# 학습
try:
keywords, sents = summarize_with_sentences(texts,
stopwords=stopwords,
num_keywords=5,
num_keysents=3)
for word, r in sorted(keywords.items(),
key=lambda x: x[1],
reverse=True)[:5]:
# print('%8s:\t%.4f' % (word, r))
# print('#%s' % word)
tag += '#' + word + ' '
except ValueError as v:
# print('#')
tag = '# '
# tag에 추가
return tag
def summary(list):
try:
penalty = lambda x: 0 if (5 <= len(x) <= 80) else 1 # 문장 길이
# stopwords = {'이러한', '일단', '제가', '이거', '아니라', '때문에',
# '동영상', '도움말', '미지원으로', '드래그', '지원되지않습니다.도움말',
# 'ㅎㅎ', '중입니다.5분', '퍼가기', 'Object', '마우스를', '인코딩', '음소',
# '음소거', 'Flash', '영상의', '소요'}
keywords, sents = summarize_with_sentences(
list,
penalty=penalty,
# stopwords=stopwords,
diversity=0.7,
num_keywords=100,
num_keysents=10,
scaling=lambda x: 1,
beta=0.85, # PageRank의 decaying factor beta
max_iter=10,
verbose=True
)
print(keywords)
return sents
except ValueError:
print('key가 없습니다.')
pass
def summarizer(self,
text,
option='krwordrank'): # 'gensim', 'textrank', 'krwordrank'
sent_lst = text.split(". ")
if option == 'krwordrank':
result = summarize_with_sentences(sent_lst, num_keysents=3)[1]
elif option == 'gensim':
result = summarize(text, ratio=3 / len(sent_lst)).split("\n")
# else:
# ks_summarizer = KeysentenceSummarizer(tokenize = self.okt.morphs)
# result = list(zip(*ks_summarizer.summarize(sents=sent_lst,topk=3)))[2]
return result
def test_keysentence(test_config):
data_path = test_config['data_path']
with open(data_path, encoding='utf-8') as f:
texts = [line.rsplit('\t')[0].strip() for line in f]
keywords, sents = summarize_with_sentences(texts,
num_keywords=100,
num_keysents=10)
for word in ['영화', '너무', '정말', '음악', '마지막']:
assert word in keywords
assert len(sents) == 10
print('\nKR-WordRank key-sentence extraction 라라랜드 영화 리뷰 10 개 핵심 문장')
for sent in sents:
print(' - {}'.format(sent))
def summary(list):
penalty = lambda x: 0 if (25 <= len(x) <= 80) else 1 #문장 길이
stopwords = {'이러한', '일단', '제가', '이거', '아니라', '때문에'}
keywords, sents = summarize_with_sentences(
list,
penalty=penalty,
stopwords=stopwords,
diversity=0.7,
num_keywords=100,
num_keysents=10,
scaling=lambda x: 1,
verbose=False,
)
return sents
def summary_text(texts):
texts = texts
penalty = lambda x: 0 if (25 <= len(x) <= 50) else 1
stopwords = {'은', '는', '이', '가', '오늘'}
keywords, sents = summarize_with_sentences(
texts,
penalty=penalty,
stopwords=stopwords,
diversity=0.7,
num_keywords=10,
num_keysents=1,
scaling=lambda x: 1,
verbose=False,
)
keyword = []
for sent in sents:
print(sent)
for i in keywords:
keyword.append(i)
return print(keyword)
#이를 토대로 우리가 좀 더 원하는건 okt(twitter) 인듯
from collections import Counter
kkma_candidates = kkma.sentences(content)
nouns = okt.nouns(content)
from krwordrank.sentence import summarize_with_sentences
keywords, sents = summarize_with_sentences(
nouns,
num_keywords=100,
num_keysents=1
)
import re
from collections import Counter
#널리 알려진 한국어 형태소 분석기들 중에선 빠르고 적절하며 원문을 보존하며 문장 구분을 해주는 기능이 구현된게 없고
def xplit(*delimiters):
return lambda value: re.split('|'.join([re.escape(delimiter) for delimiter in delimiters]), value)
xplit('. ', '? ', '! ', '\n', '.\n')("This is a sentence. Here is another sentence.\nHello, world!")
class Sentence:
stopwords = custom_stopwords + default_stopwords
return set(stopwords)
with open('../data/test4_punct.txt', 'r') as f:
text = f.read().split('\n')
text = ' '.join(text)
text = text.split('. ')
stopwords = get_stopwords()
print(stopwords)
print('====================')
keywords, sents = summarize_with_sentences(text,
stopwords=stopwords,
num_keywords=100,
diversity=0.7,
num_keysents=5,
scaling=lambda x: 1,
verbose=True)
print(list(keywords.items())[:10])
print('====================')
for i, s in enumerate(sents):
print(i, s)
print('====================')
wordrank_extractor = KRWordRank(
min_count=3, # 단어의 최소 출현 빈도수 (그래프 생성 시)
max_length=20, # 단어의 최대 길이
verbose=True)
beta = 0.85 # PageRank의 decaying factor beta
max_iter = 10
from krwordrank.sentence import summarize_with_sentences
stop = 0
test = "국어 질문있습니다."
st = ''
for r in range(len(test)):
texts = test[r]
texts = preprocessing(texts, b_idx)
st += texts
texts = st.split('. ')
try:
stopwords = {b_idx.split(' ')[0], b_idx.split(' ')[1]}
keywords, sents = summarize_with_sentences(texts,
stopwords=stopwords,
num_keywords=100,
num_keysents=10)
except ValueError:
print('key가 없습니다.')
print()
continue
for word, r in sorted(keywords.items(), key=lambda x: x[1], reverse=True)[:7]:
#print('%8s:\t%.4f' % (word, r))
print('#%s' % word)
print()
if stop == 50:
break
diversity=0.3,
topk=10)
for sent in sents:
print(sent)
from krwordrank.sentence import summarize_with_sentences
penalty = lambda x: 0 if (10 <= len(x) <= 50) else 1
stopwords = {'잘 부탁드립니다', '부탁드립니다', '잘', '정말', '진짜'}
keywords, sents = summarize_with_sentences(
texts,
penalty=penalty,
stopwords=stopwords,
diversity=0.7,
num_keywords=50,
num_keysents=10,
scaling=lambda x: 1,
verbose=False,
)
for sent in sents:
print(sent)
# json으로 변경
# decode 코드가 주피터에서 안먹긴 하는데, 아마 decode 하면 될거라고 생각
print(json.dumps(sents))
print('')
print(json.dumps(sents, indent=4))
print(type(json.dumps(sents)))
with open('words.json', 'w', encoding="utf-8") as make_file:
json.dump(sents, make_file, ensure_ascii=False, indent="\t")
# .”라고 전화고 있다 와 같은경우 방지 ( 판례 본문에서 직접 복사해와야한다.)
if content[i][j + 1] == '”':
continue
final_content.append(content[i][sentence_start:j + 1])
sentence_start = j + 1
print(k, '번째 글')
# print(final_content)
# from krwordrank.sentence import summarize_with_sentences
penalty = lambda x: 0 if (15 <= len(x) <= 90) else 1
keywords, sents = summarize_with_sentences(final_content,
penalty=penalty,
diversity=0.5,
num_keywords=100,
num_keysents=2,
verbose=False)
print(keywords)
print(sents)
driver.close()
driver.switch_to.window(old_tab)
print('----------------------------------')
driver.quit()
'''
from krwordrank.word import KRWordRank
text = text.split('.')
if ' ' in text:
text = list(filter(lambda a: a != ' ', text))
if '' in text:
text = list(filter(lambda a: a != '', text))
print(len(text), text)
keysents = ''
if len(text) <= num_keysents:
keysents = text[0]
keysents = '. '.join(keysents)
else:
keywords, keysents = summarize_with_sentences(
text,
diversity=0.7,
num_keysents=1,
scaling=lambda x: 1,
verbose=False,
)
keysents = '. '.join(keysents)
# print(keysents)
basic_loader._save_text(keysents, idx)
# score = rouge1(keywords, keysents, mecab_tokenizer)
# print(idx, score)
idx += 1
# lexrank.summarize(text)
def summary():
cur = mysql.connection.cursor()
user_email = get_jwt_identity()['user_email']
pre_data = request.get_json()['paragraph']
emotion = request.get_json()['strength_of_feeling']
created_data_time = datetime.datetime.utcnow()
data = []
data_list = []
data.append(pre_data)
for sentence in data:
list_sentence1 = sentence.split('\n')
for list_sentence2 in list_sentence1:
list_sentence = list_sentence2.replace('. ', '. ...').replace(
'? ', '? ...').replace('! ', '! ...').split(' ...')
for lines in list_sentence:
line = lines.strip()
data_list.append(line)
data_list1 = list(data_list)
for i in range(len(data_list)):
x = data_list1.count('')
for j in range(x):
data_list1.remove('')
texts = data_list1
penalty = lambda x: 0 if (10 <= len(x) <= 120) else 1
stopwords = {'오늘', '오늘은'}
keywords, sents = summarize_with_sentences(texts,
penalty=penalty,
stopwords=stopwords,
diversity=0.5,
num_keywords=7,
num_keysents=3,
scaling=lambda x: 1,
verbose=False,
min_count=1)
before_sentiment = []
sentiment = []
keyword = []
for sent in sents:
before_sentiment.append(sent)
print(before_sentiment)
def text_input(a):
global graph
with graph.as_default():
txt = []
txt.append(a)
text = []
for sentence in txt:
temp_X = []
temp_X = okt.morphs(sentence, stem=True) # 토큰화
temp_X = [word for word in temp_X
if not word in stopwords] # 불용어 제거
text.append(temp_X)
seq = tokenizer.texts_to_sequences(text)
padded = pad_sequences(seq, maxlen=max_len)
pred = model.predict(padded)
labels = [0, 1, 2, 3, 4]
return labels[np.argmax(pred)]
for i in range(3):
sentiment.append(text_input(a=before_sentiment[i]))
print(sentiment)
def find_nearest(array, value):
n = [abs(i - value) for i in array]
idx = n.index(min(n))
return idx
a = find_nearest(sentiment, emotion)
summary_text = before_sentiment[a]
cur.execute(
"INSERT INTO user_summary (user_email, summary_text, created_data_time) VALUES ('"
+ str(user_email) + "', '" + str(summary_text) + "', '" +
str(created_data_time) + "')")
mysql.connection.commit()
result = {
'user_email': user_email,
'summary_text': summary_text,
'created_data_time': created_data_time
}
return jsonify({'result': result})
