def process_mail(mail,exist=0):
title = mail.name
art_title = title
vec = mail.text
sen2 = 0
if(sen2 == 1):
input_name = title+'_sen2_test.txt'
feature_name = title+'_sen2_features.txt'
output_name = title+'_sen2_output.txt'
#vec = construct_2sen_vec(article)
else:
input_name = title+'_test.txt'
feature_name = title+'_features.txt'
output_name = title+'_output.txt'
#vec = construct_sentence_vec(article)
matrix,title_sim = cal_similarity(mail)
important = lexrank(matrix,len(vec),0.001)
important = (important-min(important))/(max(important)-min(important))
if(max(title_sim)-min(title_sim) != 0):
title_sim = (title_sim-min(title_sim))/(max(title_sim)-min(title_sim))
if(math.isnan(important[0])):
print('similarity')
print(matrix)
for k in range(len(important)):
if(math.isnan(important[k])):
important[k] = 0
return vec,important,title_sim
def lexrankSentences(match):
abstract, text = match
pattern2 = re.compile(r'<P>([\w\W]+?)</P>')
pattern3 = re.compile(r'\n')
text = pattern3.sub(" ", text)
sentences = pattern2.findall(text)
result = lexrank(sentences)
s1 = ''
s2 = ''
if len(result) >= 2:
s1 = result[0]
s2 = result[1]
return abstract, s1, s2
def oz(yontem, so, haber):
if len(haber) < 1:
print('Lütfen Cümle Giriniz')
elif so == "" or so == 0 or so == '0':
print('Lütfen Özet Uzunluğu için Sıkıştırma Oranı Giriniz')
elif len(haber) < 2:
return haber
else:
if yontem == "textrank":
ozetcumleleri = textrank(haber, so)
elif yontem == "kumeleme":
ozetcumleleri = kumeleme(haber, so)
elif yontem == "lexrank":
ozetcumleleri = lexrank(haber, so)
else:
ozetcumleleri = klasik(haber, so)
yeniozet = ' . '.join(ozetcumleleri) + ' . '
return (yeniozet)
def summarize(txtfilepath, maxlength, model):
text_file = open(txtfilepath, "r", encoding="UTF-8")
text = text_file.read()
text_file.close()
sentences = list(sent_splitter_ja(text))
rank = lexrank.lexrank(sentences, len(sentences), 0.5, "word2vec", model)
dict = {}
for i in range(len(sentences)):
dict[sentences[i]] = rank[i]
result = {}
for k, v in sorted(dict.items(), key=lambda x: -x[1]):
result[k] = v
ret = ""
keys = list(result.keys())
while len(ret + keys[0]) < maxlength:
ret += keys[0] + "\n"
keys.pop(0)
if len(keys) == 0:
break
return ret
def process_document(title,sen2=1,exist=1):
article,art_title = crawl_article(title)
#sen2 = 1
#exist = 1
if(sen2 == 1):
input_name = title+'_sen2_test.txt'
feature_name = title+'_sen2_features.txt'
output_name = title+'_sen2_output.txt'
vec = construct_2sen_vec(article)
else:
input_name = title+'_test.txt'
feature_name = title+'_features.txt'
output_name = title+'_output.txt'
vec = construct_sentence_vec(article)
'''
if(exist == 0):
construct_input_file(vec,input_name,art_title)
os.system('python takelab_sts/takelab_simple_features.py '+input_name+' > '+feature_name)
os.system('svm-predict '+feature_name+' model.txt ' + output_name)
os.system('python postprocess_scores.py '+ input_name+' ' + output_name)
matrix,title_sim = construct_similarity_matrix(output_name,len(vec))
'''
tmp = mail_struct()
tmp.name = title
tmp.text = vec
bot = re.compile(r'\_',re.S)
tmp.remove_stop_ver_title = stop_word_remove(bot.sub(' ',title))
matrix,title_sim = cal_similarity(tmp)
important = lexrank(matrix,len(vec),0.001)
for k in range(len(important)):
if(math.isnan(important[k])):
important[k] = 0
important = (important-min(important))/(max(important)-min(important))
title_sim = (title_sim-min(title_sim))/(max(title_sim)-min(title_sim))
sort_index = numpy.argsort(important)[::-1]
return vec,important,title_sim
