nltk_summarization.py

import nltk
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_frequncy = max(word_frequencies.values())

	for word in word_frequencies.keys():  
	    word_frequencies[word] = (word_frequencies[word]/maximum_frequncy)

	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