An Empirical Comparison of Web Content Extraction Algorithms

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A5SWUZ4MU" target="_blank" >RIV/00216208:11320/23:5SWUZ4MU - isvavai.cz</a>

Result on the web

<a href="https://dl.acm.org/doi/10.1145/3539618.3591920" target="_blank" >https://dl.acm.org/doi/10.1145/3539618.3591920</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3539618.3591920" target="_blank" >10.1145/3539618.3591920</a>

Result language

angličtina

Original language name

An Empirical Comparison of Web Content Extraction Algorithms

Original language description

"Main content extraction from web pages—sometimes also called boilerplate removal—has been a research topic for over two decades. Yet despite web pages being delivered in a machine-readable markup format, extracting the actual content is still a challenge today. Even with the latest HTML5 standard, which defines many semantic elements to mark content areas, web page authors do not always use semantic markup correctly or to its full potential, making it hard for automated systems to extract the relevant information. A high-precision, high-recall content extraction is crucial for downstream applications such as search engines, AI language tools, distraction-free reader modes in users’ browsers, and other general assistive technologies. For such a fundamental task, however, surprisingly few openly available extraction systems or training and benchmarking datasets exist. Even less research has gone into the rigorous evaluation and a true apples-to-apples comparison of the few extraction systems that do exist. To get a better grasp on the current state of the art in the field, we combine and clean eight existing human-labeled web content extraction datasets. On the combined dataset, we evaluate 14 competitive main content extraction systems and five baseline approaches. Finally, we build three ensembles as new state-of-the-art extraction baselines. We find that the performance of existing systems is quite genre-dependent and no single extractor performs best on all types of web pages."

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

—

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval"

ISBN

978-1-4503-9408-6

ISSN

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e-ISSN

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Number of pages

10

Pages from-to

2594-2603

Publisher name

ACM

Place of publication

Taipei Taiwan

Event location

Taipei Taiwan

Event date

Jan 1, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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