PYTHON

NLP text summarization with Luhn

from sumy.summarizers.luhn
import LuhnSummarizer
def lunh_method(text):
  parser = PlaintextParser.from_string(text, Tokenizer("english"))
summarizer_luhn = LuhnSummarizer()
summary_1 = summarizer_luhn(parser.document, 2)
dp = []
for i in summary_1:
  lp = str(i)
dp.append(lp)
final_sentence = ' '.join(dp)
return final_sentence

NLP text summarization with LSA

from sumy.summarizers.lsa
import LsaSummarizer
def lsa_method(text):
   parser = PlaintextParser.from_string(text, Tokenizer("english"))
   summarizer_lsa = LsaSummarizer()
   summary_2 = summarizer_lsa(parser.document, 2)
   dp = []
   for i in summary_2:
     lp = str(i)
   dp.append(lp)
   final_sentence = ' '.join(dp)
   return final_sentence

NLP text summarization with sumy

# Load Packages
from sumy.parsers.plaintext
import PlaintextParser
from sumy.nlp.tokenizers
import Tokenizer

# Creating text parser using tokenization
parser = PlaintextParser.from_string(text, Tokenizer("english"))

from sumy.summarizers.text_rank
import TextRankSummarizer

# Summarize using sumy TextRank
summarizer = TextRankSummarizer()
summary = summarizer(parser.document, 2)

text_summary = ""
for sentence in summary:
  text_summary += str(sentence)

print(text_summary)

NLP text summarization with LSA

from sumy.summarizers.lsa
import LsaSummarizer
def lsa_method(text):
  parser = PlaintextParser.from_string(text, Tokenizer("english"))
summarizer_lsa = LsaSummarizer()
summary_2 = summarizer_lsa(parser.document, 2)
dp = []
for i in summary_2:
  lp = str(i)
dp.append(lp)
final_sentence = ' '.join(dp)
return final_sentence
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