mySummarizer.py

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):
    start='Prior_Cases/'
    file = open(start+file_name, "r")
    filedata = file.readlines()
    article=[]
    for i in filedata:
        a = i.split(". ")
        for j in a:
            if(len(j)>2):
                article.append(j)
    sentences = []

    for sentence in article:
        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)
    
    try:
    # 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
        listToStr = '. '.join([str(elem) for elem in summarize_text])
        listToStr = listToStr.rstrip("\n") 
        listToStr=listToStr+"....."
        listToStr = listToStr.replace("\"", "")
    except:
        listToStr=None
#    print("Summarize Text: \n", listToStr)
    return listToStr