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@amirinia
Forked from edubey/Text-summarization.py
Created March 19, 2020 04:43
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Text Summarizer in Python
#!/usr/bin/env python
# coding: utf-8
from nltk.corpus import stopwords
from nltk.cluster.util import cosine_distance
import numpy as np
import networkx as nx
def read_article(file_name):
file = open(file_name, "r")
filedata = file.readlines()
article = filedata[0].split(". ")
sentences = []
for sentence in article:
print(sentence)
sentences.append(sentence.replace("[^a-zA-Z]", " ").split(" "))
sentences.pop()
return sentences
def sentence_similarity(sent1, sent2, stopwords=None):
if stopwords is None:
stopwords = []
sent1 = [w.lower() for w in sent1]
sent2 = [w.lower() for w in sent2]
all_words = list(set(sent1 + sent2))
vector1 = [0] * len(all_words)
vector2 = [0] * len(all_words)
# build the vector for the first sentence
for w in sent1:
if w in stopwords:
continue
vector1[all_words.index(w)] += 1
# build the vector for the second sentence
for w in sent2:
if w in stopwords:
continue
vector2[all_words.index(w)] += 1
return 1 - cosine_distance(vector1, vector2)
def build_similarity_matrix(sentences, stop_words):
# Create an empty similarity matrix
similarity_matrix = np.zeros((len(sentences), len(sentences)))
for idx1 in range(len(sentences)):
for idx2 in range(len(sentences)):
if idx1 == idx2: #ignore if both are same sentences
continue
similarity_matrix[idx1][idx2] = sentence_similarity(sentences[idx1], sentences[idx2], stop_words)
return similarity_matrix
def generate_summary(file_name, top_n=5):
stop_words = stopwords.words('english')
summarize_text = []
# Step 1 - Read text anc split it
sentences = read_article(file_name)
# Step 2 - Generate Similary Martix across sentences
sentence_similarity_martix = build_similarity_matrix(sentences, stop_words)
# Step 3 - Rank sentences in similarity martix
sentence_similarity_graph = nx.from_numpy_array(sentence_similarity_martix)
scores = nx.pagerank(sentence_similarity_graph)
# Step 4 - Sort the rank and pick top sentences
ranked_sentence = sorted(((scores[i],s) for i,s in enumerate(sentences)), reverse=True)
print("Indexes of top ranked_sentence order are ", ranked_sentence)
for i in range(top_n):
summarize_text.append(" ".join(ranked_sentence[i][1]))
# Step 5 - Offcourse, output the summarize texr
print("Summarize Text: \n", ". ".join(summarize_text))
# let's begin
generate_summary( "msft.txt", 2)
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