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tldr.py
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tldr.py
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# coding = utf-8
# tl;dr (too long; didn't read) -- text summarizer
import re
from nltk.tokenize import sent_tokenize, word_tokenize
class Summarizer(object):
def split_to_paragraphs(self, content):
"""
method to split the content into paragraphs
"""
return content.split("\n\n")
def split_to_sentences(self, content):
"""
method to split the content into sentences
"""
content = content.replace("\n", ". ")
return sent_tokenize(content)
def format_sentence(self, sentence):
"""
remove all non-alphabetic characters from sentence
"""
sentence = re.sub(r'\W+', '', sentence)
return sentence
def sentence_intersection(self, sent1, sent2):
"""
sentence similarity measure
"""
# splitting sentence into words/tokens
s1 = set(word_tokenize(sent1))
s2 = set(word_tokenize(sent2))
# if there's no intersection between sentences
if len(s1.intersection(s2)) == 0:
return 0
# normalizing the intersection of sentences by the avg no. of words in them
return len(s1.intersection(s2)) / ((len(s1) + len(s2)) / 2)
def rank_sentences(self, content):
"""
convert the contents into a dict(sentence, rank_of_sentence) format
"""
# split the content to sentences
sentences = self.split_to_sentences(content)
# calculate intersection of every two sentences
n = len(sentences)
values = [[0 for x in range(n)] for x in range(n)]
for i in range(0, n):
for j in range(0, n):
values[i][j] = self.sentence_intersection(sentences[i], sentences[j])
# building dict(sentence,rank) dictionary
# score of a sentence is the sum of all its intersection
sent_dict = {}
for i in range(0, n):
score = 0
for j in range(0, n):
if i == j:
continue
score = score + values[i][j]
sent_dict[self.format_sentence(sentences[i])] = score
return sent_dict
def get_best_sentences(self, paragraph, sentence_dictionary):
"""
get the best sentence in a paragraph
"""
# split paragraph to sentences
sentences = self.split_to_sentences(paragraph)
# ignore one liner paragraphs
if len(sentences) < 2:
return ""
# get best sentence acc to sentence dictionary
best_sent = ""
max_value = 0
for sent in sentences:
formated_sent = self.format_sentence(sent)
if formated_sent:
if sentence_dictionary[formated_sent] > max_value:
max_value = sentence_dictionary[formated_sent]
best_sent = sent
return best_sent
def get_summary(self, title, content, sentence_dictionary):
"""
method to get the summary
"""
# split into paragraphs
paragraphs = self.split_to_paragraphs(content)
# keep the title as it is
summary = []
summary.append(title.strip())
summary.append("")
# adding best sentences from each paragraph to summary
for paragraph in paragraphs:
sentence = self.get_best_sentences(paragraph, sentence_dictionary).strip()
if sentence:
summary.append(sentence)
return ("\n").join(summary)
if __name__ == "__main__":
title = "Text Summarizer"
with open('sample_text.txt') as file:
content = file.read()
summary_obj = Summarizer()
sentence_dictionary = summary_obj.rank_sentences(content)
summary = summary_obj.get_summary(title, content, sentence_dictionary)
print(summary)
print("")
print("Original Length %s" % (len(title) + len(content)))
print("Summary Length %s" % len(summary))
print("Summary Ratio: %s" % (100 - (100 * (len(summary) / (len(title) + len(content))))))