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Summarize algorithm adapted for Flask and PythonAnywhere (based from
# coding=UTF-8
from __future__ import division
import re, urllib2
from flask import Flask, request, jsonify
app = Flask(__name__)
def hello_world():
return 'Hello from Flask!'
def summarize():
title = urllib2.unquote(request.args.get('title'))
content = urllib2.unquote(request.args.get('content'))
st = SummaryTool()
sentences_dic = st.get_sentences_ranks(content)
summary = st.get_summary(title, content, sentences_dic)
return jsonify(result=summary)
# This is a naive text summarization algorithm
# Created by Shlomi Babluki
# April, 2013
class SummaryTool(object):
# Naive method for splitting a text into sentences
def split_content_to_sentences(self, content):
content = content.replace("\n", ". ")
return content.split(". ")
# Naive method for splitting a text into paragraphs
def split_content_to_paragraphs(self, content):
return content.split("\n\n")
# Caculate the intersection between 2 sentences
def sentences_intersection(self, sent1, sent2):
# split the sentence into words/tokens
s1 = set(sent1.split(" "))
s2 = set(sent2.split(" "))
# If there is not intersection, just return 0
if (len(s1) + len(s2)) == 0:
return 0
# We normalize the result by the average number of words
return len(s1.intersection(s2)) / ((len(s1) + len(s2)) / 2)
# Format a sentence - remove all non-alphbetic chars from the sentence
# We'll use the formatted sentence as a key in our sentences dictionary
def format_sentence(self, sentence):
sentence = re.sub(r'\W+', '', sentence)
return sentence
# Convert the content into a dictionary <K, V>
# k = The formatted sentence
# V = The rank of the sentence
def get_sentences_ranks(self, content):
# Split the content into sentences
sentences = self.split_content_to_sentences(content)
# Calculate the intersection of every two sentences
n = len(sentences)
values = [[0 for x in xrange(n)] for x in xrange(n)]
for i in range(0, n):
for j in range(0, n):
values[i][j] = self.sentences_intersection(sentences[i], sentences[j])
# Build the sentences dictionary
# The score of a sentences is the sum of all its intersection
sentences_dic = {}
for i in range(0, n):
score = 0
for j in range(0, n):
if i == j:
score += values[i][j]
sentences_dic[self.format_sentence(sentences[i])] = score
return sentences_dic
# Return the best sentence in a paragraph
def get_best_sentence(self, paragraph, sentences_dic):
# Split the paragraph into sentences
sentences = self.split_content_to_sentences(paragraph)
# Ignore short paragraphs
if len(sentences) < 2:
return ""
# Get the best sentence according to the sentences dictionary
best_sentence = ""
max_value = 0
for s in sentences:
strip_s = self.format_sentence(s)
if strip_s:
if sentences_dic[strip_s] > max_value:
max_value = sentences_dic[strip_s]
best_sentence = s
return best_sentence
# Build the summary
def get_summary(self, title, content, sentences_dic):
# Split the content into paragraphs
paragraphs = self.split_content_to_paragraphs(content)
# Add the title
summary = []
# Add the best sentence from each paragraph
for p in paragraphs:
sentence = self.get_best_sentence(p, sentences_dic).strip()
if sentence:
return ("\n").join(summary)
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