dobbersc/fundus-evaluation

[ACL 2024] Evaluation of the Fundus News Scraper

28
/ 100
Experimental

This project helps researchers and data scientists compare the quality of different news article scraping tools. It takes raw HTML content from news articles and their corresponding manually extracted 'ground truth' text. The output is a detailed evaluation, including tables and plots, that shows how accurately each scraper extracts clean, complete news article text compared to the human-verified version. Researchers and data scientists who need to reliably gather large volumes of news content for analysis would use this.

No commits in the last 6 months.

Use this if you need to objectively assess and compare the performance of various news scraping tools to ensure you're extracting high-quality, artifact-free text for your research or data pipeline.

Not ideal if you're looking for a simple, out-of-the-box news scraper for direct use without needing to benchmark its extraction quality.

news-scraping text-extraction natural-language-processing data-quality-assessment information-retrieval-evaluation
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 5 / 25
Maturity 16 / 25
Community 7 / 25

How are scores calculated?

Stars

10

Forks

1

Language

Python

License

MIT

Last pushed

Aug 14, 2024

Commits (30d)

0

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