Resumen de textos con Python (Código)

import bs4 as bs  
import urllib.request  
import re
import nltk
import bs4
import urllib.request
import requests
from bs4 import BeautifulSoup
import urllib.request
from inscriptis import get_text
from googletrans import Translator
 
#scrapea articulo de wikipedia
enlace = "https://en.wikipedia.org/wiki/Python_(programming_language)"
html = urllib.request.urlopen(enlace).read().decode('utf-8')
text = get_text(html)
article_text = text
article_text = article_text.replace("[ edit ]", "")
print ("###################")
 
from nltk import word_tokenize,sent_tokenize
# Removing Square Brackets and Extra Spaces
article_text = re.sub(r'\[[0-9]*\]', ' ', article_text)  
article_text = re.sub(r'\s+', ' ', article_text)  
 
formatted_article_text = re.sub('[^a-zA-Z]', ' ', article_text )  
formatted_article_text = re.sub(r'\s+', ' ', formatted_article_text)  
#nltk.download()
#EN ESTA PARTE HACE LA TOKENIZACION 
sentence_list = nltk.sent_tokenize(article_text)  
 
#EN ESTA PARTE ENCUENTRA LA FRECUENCIA DE CADA PALABRA
stopwords = nltk.corpus.stopwords.words('english')
 
word_frequencies = {}  
for word in nltk.word_tokenize(formatted_article_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)
 
#CALCULA LAS FRASES QUE MÁS SE REPITEN
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]
 
#REALIZA EL RESUMEN CON LAS MEJORES FRASES
import heapq  
summary_sentences = heapq.nlargest(7, sentence_scores, key=sentence_scores.get)
 
summary = ' '.join(summary_sentences)  
print(summary)  
 
###
#traducir:
################
translator = Translator()
translate = translator.translate(summary, src="en", dest="es")
print(translate.text)
############################
print("Haz mi curso gratis de python:  https://www.udemy.com/course/python-desde-0-para-principiantes/")