sumyvstrafilatura
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.
Trafilatura is a Python package and command-line tool designed to gather text on the Web. It includes discovery, extraction and text processing components. Its main applications are web crawling, downloads, scraping, and extraction of main texts, metadata and comments. It aims at staying handy and modular: no database is required, the output can be converted to various commonly used formats.
Going from raw HTML to essential parts can alleviate many problems related to text quality, first by avoiding the noise caused by recurring elements (headers, footers, links/blogroll etc.) and second by including information such as author and date in order to make sense of the data. The extractor tries to strike a balance between limiting noise (precision) and including all valid parts (recall). It also has to be robust and reasonably fast, it runs in production on millions of documents.
This tool can be useful for quantitative research in corpus linguistics, natural language processing, computational social science and beyond: it is relevant to anyone interested in data science, information extraction, text mining, and scraping-intensive use cases like search engine optimization, business analytics or information security.
Example Use
# -*- 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)
# it can be used to clean HTML files
from trafilatura import clean_html
html = '<html><head><title>My Title</title></head><body><p>This is some <b>bold</b> text.</p></body></html>'
cleaned_html = clean_html(html)
print(cleaned_html)
# can strip away tags:
clean_html(html, tags_to_remove=["title"])
# or attributes
clean_html(html, attributes_to_remove=["title"])