Evaluation

Although text is ubiquitous on the Web, extracting information from web pages can prove to be difficult. Should the tooling be adapted to particular news outlets or blogs that are targeted (which often amounts to the development of web scraping tools) or should the extraction be as generic as possible to provide opportunistic ways of gathering information?

The extraction focuses on the main content, which is usually the part displayed centrally, without the left or right bars, the header or the footer, but including potential titles and (optionally) comments. This task is also known as web scraping, boilerplate removal, DOM-based content extraction, main content identification, or web page cleaning.

Alternatives

Although a few corresponding Python packages are not actively maintained the following alternatives exist.

These packages keep the structure intact but don’t focus on main text extraction:

  • html2text converts HTML pages to Markup language
  • html_text converts HTML code to plain text
  • inscriptis converts HTML to text with a particular emphasis on nested tables

These packages focus on main text extraction:

  • boilerpy3 is a Python version of the boilerpipe algorithm for boilerplate removal and fulltext extraction
  • dragnet features combined and machine-learning approaches, but requires more dependencies and potentially fine-tuning
  • goose3 can extract information for embedded content but doesn’t preserve markup
  • jusText is designed to preserve mainly text containing full sentences along with some markup, it has been explicitly developed to create linguistic resources
  • newspaper is mostly geared towards newspaper texts, provides additional functions but no structured text or comment extraction
  • news-please is a news crawler that extracts structured information
  • python-readability cleans the page and preserves some markup

Last but not least, trafilatura is the library documented here. It downloads web pages, scrapes main text and comments while preserving some structure, and converts to TXT, CSV, XML & TEI-XML.

The tools are compared to the raw page source and to a meaningful baseline consisting of extracting all the text contained in paragraph, code or quoting elements.

Description

Test set: The experiments below are run on a collection of documents which are either typical for Internet articles (news outlets, blogs) or non-standard and thus harder to process. Some contain mixed content (lists, tables) and/or non-standard not fully valid HTML code. They were selected from large collections of web pages in German, for the sake of completeness a few documents in other languages are added (notably English, French, other European languages, Chinese and Arabic).

Evaluation: Decisive document segments are singled out which are not statistically representative but very significant in the perspective of working with the texts, most notably left/right columns, additional header, author or footer information such as imprints or addresses, as well as affiliated and social network links, in short boilerplate. Raw text segments are expected which is also a way to evaluate the quality of HTML extraction in itself.

Time: The execution time is not to be taken too seriously, the only conclusion at this stage is that goose3 and newspaper are slower than the rest while news-please’s execution time isn’t comparable because of operations unrelated to text extraction. Baseline extraction is simple and fast.

Errors: The newspaper and boilerpipe modules do not work without errors on every HTML file in the test set, probably because of malformed HTML, encoding or parsing bugs.

Results: The baseline beats a few systems, showing its interest. It turns out that rule-based approaches such as trafilatura’s obtain balanced results despite a lack of precision. Combined with an algorithmic approach they perform significantly better than the other tested solutions. justext is highly configurable and tweaking its configuration leads to better performance than its generic settings.

Roadmap: Further evaluations coming up, including additional tools and languages. Comment extraction still has to be evaluated, although most libraries don’t offer this functionality.

The evaluation script is available on the project repository: tests/comparison.py. To reproduce the tests just clone the repository, install all necessary packages and run the evaluation script with the data provided in the tests directory.

Results (2020-07-16)

400 documents, 1186 text and 1198 boilerplate segments
Python Package Precision Recall Accuracy F-Score Diff.
raw HTML 0.524 0.879 0.543 0.657 0
html2text 2020.1.16 0.485 0.718 0.480 0.579 8.4x
html_text 0.5.1 0.521 0.962 0.542 0.676 1.8x
inscriptis 1.0 (html to txt) 0.527 0.965 0.551 0.681 1.9x
newspaper3k 0.2.8 0.916 0.577 0.763 0.708 11.8x
justext 2.2.0 (tweaked) 0.867 0.651 0.777 0.744 4.9x
goose3 3.1.6 0.953 0.635 0.803 0.762 17.3x
baseline (text markup) 0.738 0.804 0.760 0.770 1x
boilerpy3 1.0.2 (article mode) 0.847 0.711 0.792 0.773 4.4x
dragnet 2.0.4 0.906 0.704 0.816 0.792 2.8x
readability-lxml 0.8.1 0.913 0.739 0.835 0.817 5.4x
news-please 1.4.25 0.918 0.739 0.837 0.819 56.4x
trafilatura 0.5.1 0.927 0.854 0.894 0.889 3.1x
trafilatura 0.5.1 (+ fallbacks) 0.933 0.885 0.911 0.908 6.8x

External evaluations

Older results (2020-03-19)

300 documents, 869 text and 878 boilerplate segments
Python Package Precision Recall Accuracy F-Score Time
raw HTML 0.519 0.885 0.535 0.654 0
baseline (text markup) 0.726 0.776 0.742 0.750 1.14
html2text 2020.1.16 0.499 0.787 0.501 0.611 11.00
inscriptis 1.0 (html to txt) 0.521 0.962 0.541 0.676 2.47
justext 2.2.0 (German stoplist) 0.849 0.529 0.719 0.652 6.37
newspaper 0.2.8 0.923 0.591 0.772 0.721 14.80
goose3 3.1.6 0.957 0.640 0.807 0.767 21.54
boilerpy3 1.0.2 (article mode) 0.841 0.734 0.799 0.784 5.65
dragnet 2.0.4 0.909 0.722 0.825 0.804 3.64
readability-lxml 0.7.1 0.928 0.743 0.844 0.826 6.59
news-please 1.4.25 0.926 0.747 0.844 0.827 70.81
trafilatura 0.3.1 (rule-based) 0.901 0.831 0.871 0.865 5.43
trafilatura 0.3.1 (+ justext) 0.897 0.868 0.884 0.882 6.97
trafilatura 0.4 0.914 0.869 0.894 0.891 4.87
trafilatura 0.4 (+ fallback) 0.925 0.904 0.916 0.914 9.94

Older results (2020-01-29)

100 documents, 266 text and 294 boilerplate segments
Python Package Precision Recall Accuracy F-Score Time
raw HTML 0.492 0.902 0.511 0.637 0
inscriptis 1.0 (html to txt) 0.504 0.989 0.532 0.668 0.87
justext 2.2.0 (German stoplist) 0.886 0.553 0.754 0.681 2.22
goose3 3.1.6 0.935 0.594 0.787 0.726 7.64
newspaper 0.2.8 0.920 0.609 0.789 0.733 5.34
boilerpy3 1.0.2 (default mode) 0.767 0.756 0.775 0.761 1.89
dragnet 2.0.4 0.904 0.673 0.811 0.772 1.25
readability-lxml 0.7.1 0.894 0.699 0.818 0.785 2.34
news-please 1.4.25 0.900 0.714 0.827 0.797 22.99
trafilatura 0.3.1 (rule-based) 0.872 0.895 0.887 0.883 1.87
trafilatura 0.3.1 (+ justext) 0.889 0.936 0.914 0.912 2.19