text_summarizer.py

''' text summarizer model using nltk '''

import nltk
from nltk.corpus import stopwords
from nltk.cluster.util import cosine_distance
import numpy as np
import networkx as nx
 
def read_article(filedata):
    # file = open(file_name, "r")
    # filedata = file.readlines()
    article = filedata.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(filedata, top_n=5):
    nltk.download("stopwords")
    stop_words = stopwords.words('english')
    summarize_text = []

    # Read text and split it
    sentences =  read_article(filedata)

    # Generate Similary Martix across sentences
    sentence_similarity_martix = build_similarity_matrix(sentences, stop_words)

    # Rank sentences in similarity martix
    sentence_similarity_graph = nx.from_numpy_array(sentence_similarity_martix)
    scores = nx.pagerank(sentence_similarity_graph)

    # 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]))

    # Output summary
    # f = open("Model\\sample_summary.txt", "w")
    # f.write( ". ".join(summarize_text))
    # f.close()
    summary_text =  ". ".join(summarize_text)
    return summary_text

# run test harness
# generate_summary( "Model\\fb.txt", 2)