resumidor.py

#Código Limpo 17-04-2019
import nltk
nltk.download()
import urllib as urllib 
from urllib.request import Request,urlopen
link = Request('http://ultimosegundo.ig.com.br/politica/2017-04-25/reforma-da-previdencia.html',headers={'User-Agent' :'Google Chrome/67'})
pagina = urlopen(link) .read() .decode('utf-8', 'ignore')
from bs4 import BeautifulSoup
soup = BeautifulSoup(pagina, "html.parser")
texto = soup.find(id="noticia").text
from nltk.tokenize import word_tokenize
from nltk.tokenize import sent_tokenize
sentencas = sent_tokenize(texto)
palavras = word_tokenize(texto.lower())
from nltk.corpus import stopwords
from string import punctuation
stopwords = set(stopwords.words('portuguese') + list(punctuation))
palavras_sem_stopwords = [palavra for palavra in palavras if palavra not in stopwords]

from nltk.probability import FreqDist
frequencia = FreqDist(palavras_sem_stopwords)
from collections import defaultdict
sentencas_importantes = defaultdict(int)

for i, sentenca in enumerate(sentencas):
    for palavra in word_tokenize(sentenca.lower()):
         if palavra in frequencia: sentencas_importantes[i] += frequencia[palavra]
            
from heapq import nlargest
idx_sentencas_importantes = nlargest(4, sentencas_importantes,sentencas_importantes.get)
for i in sorted(idx_sentencas_importantes):print(sentencas[i])