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Main Authors: Dallabetta, Max, Dobberstein, Conrad, Breiding, Adrian, Akbik, Alan
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2403.15279
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author Dallabetta, Max
Dobberstein, Conrad
Breiding, Adrian
Akbik, Alan
author_facet Dallabetta, Max
Dobberstein, Conrad
Breiding, Adrian
Akbik, Alan
contents This paper introduces Fundus, a user-friendly news scraper that enables users to obtain millions of high-quality news articles with just a few lines of code. Unlike existing news scrapers, we use manually crafted, bespoke content extractors that are specifically tailored to the formatting guidelines of each supported online newspaper. This allows us to optimize our scraping for quality such that retrieved news articles are textually complete and without HTML artifacts. Further, our framework combines both crawling (retrieving HTML from the web or large web archives) and content extraction into a single pipeline. By providing a unified interface for a predefined collection of newspapers, we aim to make Fundus broadly usable even for non-technical users. This paper gives an overview of the framework, discusses our design choices, and presents a comparative evaluation against other popular news scrapers. Our evaluation shows that Fundus yields significantly higher quality extractions (complete and artifact-free news articles) than prior work. The framework is available on GitHub under https://github.com/flairNLP/fundus and can be simply installed using pip.
format Preprint
id arxiv_https___arxiv_org_abs_2403_15279
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Fundus: A Simple-to-Use News Scraper Optimized for High Quality Extractions
Dallabetta, Max
Dobberstein, Conrad
Breiding, Adrian
Akbik, Alan
Computation and Language
Information Retrieval
This paper introduces Fundus, a user-friendly news scraper that enables users to obtain millions of high-quality news articles with just a few lines of code. Unlike existing news scrapers, we use manually crafted, bespoke content extractors that are specifically tailored to the formatting guidelines of each supported online newspaper. This allows us to optimize our scraping for quality such that retrieved news articles are textually complete and without HTML artifacts. Further, our framework combines both crawling (retrieving HTML from the web or large web archives) and content extraction into a single pipeline. By providing a unified interface for a predefined collection of newspapers, we aim to make Fundus broadly usable even for non-technical users. This paper gives an overview of the framework, discusses our design choices, and presents a comparative evaluation against other popular news scrapers. Our evaluation shows that Fundus yields significantly higher quality extractions (complete and artifact-free news articles) than prior work. The framework is available on GitHub under https://github.com/flairNLP/fundus and can be simply installed using pip.
title Fundus: A Simple-to-Use News Scraper Optimized for High Quality Extractions
topic Computation and Language
Information Retrieval
url https://arxiv.org/abs/2403.15279