def summarize4(self, df):
#http://ai.intelligentonlinetools.com/ml/text-summarization/
LANGUAGE = "english"
SENTENCES_COUNT = 10
stopwords = nltk.corpus.stopwords.words('english')
for row in df['conclusion']:
if row == '0' or row == '':
continue
parser = PlaintextParser(row, Tokenizer(LANGUAGE))
print("--LsaSummarizer--")
summarizer = LsaSummarizer()
summarizer = LsaSummarizer(Stemmer(LANGUAGE))
summarizer.stop_words = get_stop_words(LANGUAGE)
for sentence in summarizer(parser.document, SENTENCES_COUNT):
print(sentence)
print("--LuhnSummarizer--")
summarizer = LuhnSummarizer()
summarizer.stop_words = stopwords
for sentence in summarizer(parser.document, SENTENCES_COUNT):
print(sentence)
print("--EdmundsonSummarizer--")
summarizer = EdmundsonSummarizer()
words = ("deep", "learning", "neural")
summarizer.bonus_words = words
words = (
"another",
"and",
"some",
"next",
)
summarizer.stigma_words = words
words = (
"another",
"and",
"some",
"next",
)
summarizer.null_words = words
for sentence in summarizer(parser.document, SENTENCES_COUNT):
print(sentence)
summary2+=str(sentence)
summary2+=" "
with open("summarised_text.txt", "a", encoding="utf8") as myfile:
myfile.write("\n\nLuhn:\n")
myfile.write(summary2)
summary3 = ""
print("\n\n")
print ("--EdmundsonSummarizer--")
summarizer = EdmundsonSummarizer()
words = ("deep", "learning", "neural" )
summarizer.bonus_words = words
words = ("another", "and", "some", "next",)
summarizer.stigma_words = words
words = ("another", "and", "some", "next",)
summarizer.null_words = words
for sentence in summarizer(parser.document, SENTENCES_COUNT):
summary3+=str(sentence)
summary3+=" "
with open("summarised_text.txt", "a", encoding="utf8") as myfile:
myfile.write("\n\nEdmundson:\n")
myfile.write(summary3)
summary4 = ""
print("\n\n")
print ("--LexRankSummarizer--")
