trafilatura

Python & Command-line tool to gather text and metadata on the Web: Crawling, scraping, extraction, output as CSV, JSON, HTML, MD, TXT, XML

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Trafilatura: Discover and Extract Text Data on the Web


Trafilatura Logo

Python package
Python versions
Documentation Status
Code Coverage
Downloads
Reference DOI: 10.18653/v1/2021.acl-demo.15


Demo as GIF image

Introduction

Trafilatura is a cutting-edge Python package and command-line tool
designed to gather text on the Web and simplify the process of turning
raw HTML into structured, meaningful data. It includes all necessary
discovery and text processing components to perform 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 commonly used formats.

Going from HTML bulk to essential parts can alleviate many problems
related to text quality, by focusing on the actual content,
avoiding the noise caused by recurring elements (headers, footers
etc.), and making sense of the data with selected information. The
extractor is designed to be robust and reasonably fast, it runs in
production on millions of documents.

The tool’s versatility makes it useful for quantitative and
data-driven approaches. It is used in the academic domain and beyond
(e.g. in natural language processing, computational social science,
search engine optimization, and information security).

Features

  • Advanced web crawling and text discovery:

    • Support for sitemaps (TXT, XML) and feeds (ATOM, JSON, RSS)
    • Smart crawling and URL management (filtering and deduplication)

  • Parallel processing of online and offline input:

    • Live URLs, efficient and polite processing of download queues
    • Previously downloaded HTML files and parsed HTML trees

  • Robust and configurable extraction of key elements:

    • Main text (common patterns and generic algorithms like jusText and readability)
    • Metadata (title, author, date, site name, categories and tags)
    • Formatting and structure: paragraphs, titles, lists, quotes, code, line breaks, in-line text formatting
    • Optional elements: comments, links, images, tables

  • Multiple output formats:

    • TXT and Markdown
    • CSV
    • JSON
    • HTML, XML and XML-TEI

  • Optional add-ons:

    • Language detection on extracted content
    • Speed optimizations

  • Actively maintained with support from the open-source community:

    • Regular updates, feature additions, and optimizations
    • Comprehensive documentation

Evaluation and alternatives

Trafilatura consistently outperforms other open-source libraries in text
extraction benchmarks, showcasing its efficiency and accuracy in
extracting web content. The extractor tries to strike a balance between
limiting noise and including all valid parts.

For more information see the benchmark section
and the evaluation readme
to run the evaluation with the latest data and packages.

750 documents, 2236 text & 2250 boilerplate segments (2022-05-18), Python 3.8

Python Package Precision Recall Accuracy F-Score Diff.
html_text 0.5.2 0.529 0.958 0.554 0.682 2.2x
inscriptis 2.2.0 (html to txt) 0.534 0.959 0.563 0.686 3.5x
newspaper3k 0.2.8 0.895 0.593 0.762 0.713 12x
justext 3.0.0 (custom) 0.865 0.650 0.775 0.742 5.2x
boilerpy3 1.0.6 (article mode) 0.814 0.744 0.787 0.777 4.1x
baseline (text markup) 0.757 0.827 0.781 0.790 1x
goose3 3.1.9 0.934 0.690 0.821 0.793 22x
readability-lxml 0.8.1 0.891 0.729 0.820 0.801 5.8x
news-please 1.5.22 0.898 0.734 0.826 0.808 61x
readabilipy 0.2.0 0.877 0.870 0.874 0.874 248x
trafilatura 1.2.2 (standard) 0.914 0.904 0.910 0.909 7.1x

Other evaluations:

Usage and documentation

Getting started with Trafilatura
is straightforward. For more information and detailed guides, visit
Trafilatura’s documentation:

Youtube playlist with video tutorials in several languages:

License

This package is distributed under the Apache 2.0 license.

Versions prior to v1.8.0 are under GPLv3+ license.

Contributing

Contributions of all kinds are welcome. Visit the Contributing
page
for more information. Bug reports can be filed on the dedicated issue
page.

Many thanks to the
contributors
who extended the docs or submitted bug reports, features and bugfixes!

Context

Developed with practical applications of academic research in mind, this
software is part of a broader effort to derive information from web
documents. Extracting and pre-processing web texts to the exacting
standards of scientific research presents a substantial challenge. This
software package simplifies text data collection and enhances corpus
quality, it is currently used to build text databases for linguistic
research.

Trafilatura is an Italian word for wire
drawing symbolizing the
refinement and conversion process. It is also the way shapes of pasta
are formed.

Author

Reach out via ia the software repository or the contact
page for inquiries, collaborations, or
feedback. See also social networks for the latest updates.

This work started as a PhD project at the crossroads of linguistics and
NLP, this expertise has been instrumental in shaping Trafilatura over
the years. It has first been released under its current form in 2019,
its development is referenced in the following publications:

Citing Trafilatura

Trafilatura is widely used in the academic domain, chiefly for data
acquisition. Here is how to cite it:

Reference DOI: 10.18653/v1/2021.acl-demo.15
Zenodo archive DOI: 10.5281/zenodo.3460969

@inproceedings{barbaresi-2021-trafilatura,
  title = {{Trafilatura: A Web Scraping Library and Command-Line Tool for Text Discovery and Extraction}},
  author = "Barbaresi, Adrien",
  booktitle = "Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations",
  pages = "122--131",
  publisher = "Association for Computational Linguistics",
  url = "https://aclanthology.org/2021.acl-demo.15",
  year = 2021,
}

Software ecosystem

Case studies and publications are listed on the Used By documentation
page.

Jointly developed plugins and additional packages also contribute to the
field of web data extraction and analysis:

Software ecosystem

Corresponding posts can be found on Bits of
Language. The
blog covers a range of topics from technical how-tos, updates on new
features, to discussions on text mining challenges and solutions.

Impressive, you have reached the end of the page: Thank you for your
interest!