Downloads web pages, scrapes main text and comments while preserving some structure, and converts to TXT, CSV, JSON and XML
Trafilatura is a Python package and command-line tool which seamlessly downloads, parses, and scrapes web page data: it can extract metadata, main body text and comments while preserving parts of the text formatting and page structure. The output can be converted to different formats.
Distinguishing between a whole page and the page’s essential parts can help to alleviate many quality problems related to web text processing, by dealing with the noise caused by recurring elements (headers and footers, ads, links/blogroll, etc.).
The extractor aims to be precise enough in order not to miss texts or to discard valid documents. In addition, it must be robust, but also reasonably fast. With these objectives in mind, Trafilatura is designed to run in production on millions of web documents.
- Seamless online (including page retrieval) or parallelized offline processing using URLs, HTML files or parsed HTML trees as input
- Robust extraction algorithm, using and readability and jusText as fallback; reasonably efficient with lxml:
- Focuses on the document’s main text and/or comments
- Structural elements preserved: paragraphs, titles, lists, quotes, code, line breaks, in-line text formatting (experimental)
- Extraction of metadata (title, author, date, site name, categories and tags)
- URL lists:
- Generation of link lists from ATOM/RSS feeds
- Efficient processing of URL queues
- Blacklists or already processed URLs
- Optional language detection on extracted content
Evaluation and alternatives
For more detailed results see the evaluation page and evaluation script. 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.
|400 documents, 1186 text and 1198 boilerplate segments (2020-07-16)|
|justext 2.2.0 (tweaked)||0.867||0.651||0.777||0.744||4.9x|
|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|
|trafilatura 0.5.1 (+ fallbacks)||0.933||0.885||0.911||0.908||6.8x|
- Trafilatura is the most efficient open-source library in ScrapingHub’s article extraction benchmark.
- Best overall tool according to Gaël Lejeune & Adrien Barbaresi, Bien choisir son outil d’extraction de contenu à partir du Web (2020, PDF, French).
Primary method is with Python package manager: pip install --upgrade trafilatura.
For more details please read the installation documentation.
With Python or on the command-line.
In a nutshell, with Python:
>>> import trafilatura >>> downloaded = trafilatura.fetch_url('https://github.blog/2019-03-29-leader-spotlight-erin-spiceland/') >>> trafilatura.extract(downloaded) # outputs main content and comments as plain text ...
On the command-line:
$ trafilatura -u "https://github.blog/2019-03-29-leader-spotlight-erin-spiceland/" # outputs main content and comments as plain text ...
For more information please refer to the usage documentation.
trafilatura is distributed under the GNU General Public License v3.0. If you wish to redistribute this library but feel bounded by the license conditions please try interacting at arms length, multi-licensing with compatible licenses, or contacting me.
Online documentation: trafilatura.readthedocs.io
- In order to gather web documents, it can be useful to download the portions of a website programmatically, here is how to use sitemaps to crawl websites
- Content von Webseiten laden mit Trafilatura (Tutorial video in German by Simon Meier-Vieracker)
- Download von Web-Daten & Daten aufbereiten und verwalten (Tutorials in German by Noah Bubenhofer)
- [X] Language detection on the extracted content
- [-] Duplicate detection at sentence, paragraph and document level using a least recently used (LRU) cache
- [-] URL lists and document management
- [ ] Sitemaps processing
- [ ] Interaction with web archives (notably WARC format)
- [ ] Configuration and extraction parameters
- [ ] Integration of natural language processing tools
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