news-crawler-LM: A Small Long-Context Model For High-Quality News Crawling
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Computer Science > Computation and Language
Title:news-crawler-LM: A Small Long-Context Model For High-Quality News Crawling
Abstract:Extracting structured content from news pages remains challenging due to heterogeneous HTML layouts, inconsistent markup, and substantial boilerplate such as navigation elements and advertisements. Rule-based news crawlers can achieve high extraction accuracy by encoding site-specific structure, but require manual configuration in order to generalize to new publishers. Large language models provide a more flexible alternative by reducing the need for handcrafted rules, but their high computational cost limits practical deployment. In this paper, we introduce news-crawler-LM, a small long-context language model fine-tuned on high-quality, human-validated extractions from the Fundus news-crawling library. Our model converts raw HTML into plaintext and structured JSON, including fields such as headline, author, publication date, and article body. In our experiments, news-crawler-LM outperforms strong baselines in HTML-to-Markdown and HTML-to-JSON extraction, improving performance by +4.8 BLEU and +6.1 METEOR in the HTML-to-Markdown task, and by +2.2 BLEU and +4.1 METEOR in the HTML-to-JSON task. However, we also observe that our model only slightly better compared to other rule-based parsing libraries on the HTML-to-plaintext task in evaluations on previously unseen publishers. We release all models and artifacts to the research community.
| Comments: | KONVENS 2026 |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2607.21284 [cs.CL] |
| (or arXiv:2607.21284v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2607.21284
arXiv-issued DOI via DataCite (pending registration)
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