nltk_summarization.py

import nltk

nltk.download('punkt')
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize, sent_tokenize
import heapq


def nltk_summarizer(raw_text):
    stopWords = set(stopwords.words("english"))
    word_frequencies = {}
    for word in nltk.word_tokenize(raw_text):
        if word not in stopWords:
            if word not in word_frequencies.keys():
                word_frequencies[word] = 1
            else:
                word_frequencies[word] += 1

    maximum_frequency = max(word_frequencies.values())
    for word in word_frequencies.keys():
        word_frequencies[word] = (word_frequencies[word]/maximum_frequency)
    sentence_list = nltk.sent_tokenize(raw_text)
    sentence_scores = {}
    for sent in sentence_list:
        for word in nltk.word_tokenize(sent.lower()):
            if word in word_frequencies.keys():
                if len(sent.split(' ')) < 30:
                    if sent not in sentence_scores.keys():
                        sentence_scores[sent] = word_frequencies[word]
                    else:
                        sentence_scores[sent] += word_frequencies[word]



    summary_sentences = heapq.nlargest(7, sentence_scores, key=sentence_scores.get)

    summary = ' '.join(summary_sentences)
    return summary