The boilerpipe library provides algorithms to detect and remove the surplus "clutter" (boilerplate, templates) around the main textual content of a web page.

This project is native python port of Boilerpipe Java library by Christian Kohlschütter

The algorithms used by the library are based on (and extending) some concepts of the paper "Boilerplate Detection using Shallow Text Features" by Christian Kohlschütter et al.

• an HTML parser that transforms HTML into an internal text-only document model supporting "blocks" of text.
  • Python version uses fast lxml parsers
• several Filter components analyze and tag these text blocks
• extractors consisting of one or more Filters. Such "pipelines" take the parsed document object and distill the main textual content from it
  • one extractor in python version atm
• an HTML highlighter to visually inspect the extracted main content within a copy of the input page.
  • not supported atm

• Simple run ./url2article.py with url as a parameter
  • It will save article-name.txt with extracted text data
• Tested with cnn.com, gazeta.ru , lenta.ru, roem.ru
  • Parsed examples in doc directory
• on windows you can view files with browser(utf8 turned on) or nice text editor, Notepad is ugly

• http://boilerpipepy-youra.rhcloud.com/

• Unit tests
• More filters
• More extractors
• Different data extraction support:
  • images
  • video
  • links

• 0.001 - 23.03.2014