readabilityvssumy
python-readability is a python package that allows developers to extract the main content of a web page, removing any unnecessary or unwanted elements, such as ads, navigation, and sidebars.
It is based on the algorithm used by the popular web-based service, Readability, and it uses the beautifulsoup4 package to parse the HTML and extract the main content.
Readability is similar to Newspaper in terms that it's extracting HTML data
sumy is a Python library for automatic summarization of text documents. It can be used to extract summaries from various input formats such as plaintext, HTML, and URLs. It supports multiple languages and multiple summarization algorithms, including Latent Semantic Analysis (LSA), Luhn, Edmundson, TextRank, and SumBasic.
Example Use
import requests
from readability import document
response = requests.get('http://example.com')
doc = document(response.content)
doc.title()
'example domain'
doc.summary()
"""<html><body><div><body id="readabilitybody">\n<div>\n <h1>example domain</h1>\n
<p>this domain is established to be used for illustrative examples in documents. you may
use this\n domain in examples without prior coordination or asking for permission.</p>
\n <p><a href="http://www.iana.org/domains/example">more information...</a></p>\n</div>
\n</body>\n</div></body></html>"""
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division, print_function, unicode_literals
from sumy.parsers.html import HtmlParser
from sumy.parsers.plaintext import PlaintextParser
from sumy.nlp.tokenizers import Tokenizer
from sumy.summarizers.lsa import LsaSummarizer as Summarizer
from sumy.nlp.stemmers import Stemmer
from sumy.utils import get_stop_words
LANGUAGE = "english"
SENTENCES_COUNT = 10
if __name__ == "__main__":
url = "https://en.wikipedia.org/wiki/Automatic_summarization"
parser = HtmlParser.from_url(url, Tokenizer(LANGUAGE))
# or for plain text files
# parser = PlaintextParser.from_file("document.txt", Tokenizer(LANGUAGE))
# parser = PlaintextParser.from_string("Check this out.", Tokenizer(LANGUAGE))
stemmer = Stemmer(LANGUAGE)
summarizer = Summarizer(stemmer)
summarizer.stop_words = get_stop_words(LANGUAGE)
for sentence in summarizer(parser.document, SENTENCES_COUNT):
print(sentence)