Cleaner Pretraining Corpus Curation with Neural Web Scraping

• URL: http://arxiv.org/abs/2402.14652v3
• Date: Sat, 15 Jun 2024 03:05:54 GMT
• Title: Cleaner Pretraining Corpus Curation with Neural Web Scraping
• Authors: Zhipeng Xu, Zhenghao Liu, Yukun Yan, Zhiyuan Liu, Ge Yu, Chenyan Xiong,
• Abstract summary: This paper presents a simple, fast, and effective Neural web Scraper (NeuScraper) to help extract primary and clean text contents from webpages. Experimental results show that NeuScraper surpasses the baseline scrapers by achieving more than a 20% improvement.
• Score: 39.97459187762505
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The web contains large-scale, diverse, and abundant information to satisfy the information-seeking needs of humans. Through meticulous data collection, preprocessing, and curation, webpages can be used as a fundamental data resource for language model pretraining. However, when confronted with the progressively revolutionized and intricate nature of webpages, rule-based/feature-based web scrapers are becoming increasingly inadequate. This paper presents a simple, fast, and effective Neural web Scraper (NeuScraper) to help extract primary and clean text contents from webpages. Experimental results show that NeuScraper surpasses the baseline scrapers by achieving more than a 20% improvement, demonstrating its potential in extracting higher-quality data to facilitate the language model pretraining. All of the code is available at https://github.com/OpenMatch/NeuScraper.

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