PyReadability 0.4.0

fast python port of arc90's readability tool

This code is under the Apache License 2.0. http://www.apache.org/licenses/LICENSE-2.0

This is a python port of a ruby port of arc90’s readability project

http://lab.arc90.com/experiments/readability/

In few words, Given a html document, it pulls out the main body text and cleans it up. It also can clean up title based on latest readability.js code.

Based on:

• Latest readability.js ( https://github.com/MHordecki/readability-redux/blob/master/readability/readability.js )

• Ruby port by starrhorne and iterationlabs

• Python port by gfxmonk ( https://github.com/gfxmonk/python-readability , based on BeautifulSoup )

• Decruft effort to move to lxml ( http://www.minvolai.com/blog/decruft-arc90s-readability-in-python/ )

• “BR to P” fix from readability.js which improves quality for smaller texts.

• Github users contributions.

Installation:

easy_install readability-lxml
or
pip install readability-lxml

Usage:

from readability.readability import Document
import urllib
html = urllib.urlopen(url).read()
readable_article = Document(html).summary()
readable_title = Document(html).short_title()

Command-line usage:

python -m readability.readability -u http://pypi.python.org/pypi/readability-lxml

Using positive/negative keywords example:

python -m readability.readability -p intro -n newsindex,homepage-box,news-section -u http://python.org

Document() kwarg options:

• attributes:

• debug: output debug messages

• min_text_length:

• retry_length:

• url: will allow adjusting links to be absolute

• positive_keywords: the list of positive search patterns in classes and ids, for example: [“news-item”, “block”]

• negative_keywords: the list of negative search patterns in classes and ids, for example: [“mysidebar”, “related”, “ads”]

Updates

• 0.2.5 Update setup.py for uploading .tar.gz to pypi

• 0.2.6 Don’t crash on documents with no title

• 0.2.6.1 Document.short_title() properly works

• 0.3 Added Document.encoding, positive_keywords and negative_keywords