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).
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Advanced web crawling and text discovery:
- Support for sitemaps (TXT, XML) and feeds (ATOM, JSON, RSS)
- Smart crawling and URL management (filtering and deduplication)
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Parallel processing of online and offline input:
- Live URLs, efficient and polite processing of download queues
- Previously downloaded HTML files and parsed HTML trees
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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
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Multiple output formats:
- TXT and Markdown
- CSV
- JSON
- HTML, XML and XML-TEI
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Optional add-ons:
- Language detection on extracted content
- Speed optimizations
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Actively maintained with support from the open-source community:
- Regular updates, feature additions, and optimizations
- Comprehensive documentation
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 |
- Most efficient open-source library in ScrapingHub's article extraction benchmark
- Best overall tool according to Bien choisir son outil d'extraction de contenu à partir du Web (Lejeune & Barbaresi 2020)
- Best single tool by ROUGE-LSum Mean F1 Page Scores in An Empirical Comparison of Web Content Extraction Algorithms (Bevendorff et al. 2023)
Getting started with Trafilatura is straightforward. For more information and detailed guides, visit Trafilatura's documentation:
- Installation
- Usage: On the command-line, With Python, With R
- Core Python functions
- Interactive Python Notebook: Trafilatura Overview
- Tutorials and use cases
Youtube playlist with video tutorials in several languages:
This package is distributed under the Apache 2.0 license.
Versions prior to v1.8.0 are under GPLv3+ license.
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!
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.
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:
- Barbaresi, A. Trafilatura: A Web Scraping Library and Command-Line Tool for Text Discovery and Extraction, Proceedings of ACL/IJCNLP 2021: System Demonstrations, 2021, p. 122-131.
- Barbaresi, A. "Generic Web Content Extraction with Open-Source Software", Proceedings of KONVENS 2019, Kaleidoscope Abstracts, 2019.
- Barbaresi, A. "Efficient construction of metadata-enhanced web corpora", Proceedings of the 10th Web as Corpus Workshop (WAC-X), 2016.
Trafilatura is widely used in the academic domain, chiefly for data acquisition. Here is how to cite it:
@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,
}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:
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.
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