Downloads web pages, scrapes main text and comments while preserving some structure, and converts to TXT, CSV, XML & TEI-XML
Project description
Description
Trafilatura is 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 part of the text formatting and page structure. The output can be converted to different formats.
Distinguishing between whole page and essential parts can help to alleviate many quality problems related to web texts as it deals with the noise consisting of recurring elements (headers and footers, ads, links/blogroll).
The extractor has to be precise enough not to miss texts or discard valid documents, robust but also reasonably fast. It is designed to run in production on millions of web documents.
Features
- Seamless online (including page retrieval) or parallelized offline processing:
URLs, HTML files or parsed HTML trees as input
- Several output formats supported:
Plain text (minimal formatting)
CSV (with metadata, tab-separated values)
XML (for metadata and structure)
- Robust extraction algorithm, using and readability and jusText as fallback, reasonably efficient with lxml:
Focus on 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 queue
Blacklists or already processed URLs
Optional language detection on the 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.
300 documents, 869 text and 878 boilerplate segments (2020-03-19) |
|||||
|---|---|---|---|---|---|
Python Package |
Precision |
Recall |
Accuracy |
F-Score |
Time |
baseline (text markup) |
0.726 |
0.776 |
0.742 |
0.750 |
1.14 |
justext 2.2.0 (German stoplist) |
0.849 |
0.529 |
0.719 |
0.652 |
6.37 |
newspaper3k 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 0.4 |
0.914 |
0.869 |
0.894 |
0.891 |
4.87 |
trafilatura 0.4 (+ fallback) |
0.925 |
0.904 |
0.916 |
0.914 |
9.94 |
Installation
Chiefly with Python package managers: pip install --upgrade trafilatura.
For more details please read the installation documentation.
Usage
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.
License
trafilatura is distributed under the GNU General Public License v3.0
Going further
Online documentation: trafilatura.readthedocs.io
Trafilatura: Italian word for wire drawing.
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)
Roadmap
[-] 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
[ ] Configuration and extraction parameters
[ ] Integration of natural language processing tools
Contributing
Contributions are welcome!
Feel free to file issues on the dedicated page. Thanks to the contributors who submitted features and bugfixes!
Author
This effort is part of methods to derive information from web documents in order to build text databases for research (chiefly linguistic analysis and natural language processing). A significant challenge resides in the ability to extract and pre-process web texts to meet scientific expectations: Web corpus construction involves numerous design decisions, and this software packages can help facilitate collection and enhance corpus quality.
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.
You can contact me via my contact page or GitHub.
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