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summariser.py
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summariser.py
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from nltk.corpus import stopwords
from nltk.cluster.util import cosine_distance
import numpy as np
import networkx as nx
import math
def read_article(filename):
file = open(filename, encoding="utf-8")
filedata = file.readlines()
article = filedata[0].split(". ")
sentences = []
for sentence in article:
sentences.append(sentence)
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]
totalwords = list(set(sent1+sent2))
v1 = [0] * len(totalwords)
v2 = [0]*len(totalwords)
for w in sent1:
if w in stopwords:
continue
v1[totalwords.index(w)] += 1
for w in sent2:
if w in stopwords:
continue
v2[totalwords.index(w)] += 1
return 1 - cosine_distance(v1, v2)
def buildmatrix(sentences, stopwords):
matrix = np.zeros((len(sentences), len(sentences)))
for i in range(len(sentences)):
for j in range(len(sentences)):
if i == j:
continue
matrix[i, j] = sentence_similarity(sentences[i], sentences[j])
return matrix
def summary(filename, top_n=5):
stop_words = stopwords.words('english')
summary = []
sentences = read_article(filename)
top_n = math.ceil(0.2*len(sentences))
matrix = buildmatrix(sentences, stopwords)
graph = nx.from_numpy_array(matrix)
scores = nx.pagerank(graph)
rankedsentences = sorted(((scores[i], s) for i, s in enumerate(sentences)),
reverse=True)
#print("Indexes of top ranked sentences are : ", rankedsentences)
for i in range(top_n):
summary.append(rankedsentences[i][1])
print("Summary is: \n", ". ".join(summary))
summary('finaltext', 5)