Dripper: Token-Efficient Main HTML Extraction with a Lightweight LM

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AMM2TF5AA" target="_blank" >RIV/00216208:11320/26:MM2TF5AA - isvavai.cz</a>

Result on the web

<a href="http://arxiv.org/abs/2511.23119" target="_blank" >http://arxiv.org/abs/2511.23119</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.48550/arXiv.2511.23119" target="_blank" >10.48550/arXiv.2511.23119</a>

Result language

angličtina

Original language name

Dripper: Token-Efficient Main HTML Extraction with a Lightweight LM

Original language description

Accurately and efficiently extracting main content from general web pages is of great significance for obtaining training data for large models. Using well-pre-trained decoder-only generative language models offers excellent document comprehension capabilities, thereby effectively enhancing parsing quality. However, it remains constrained by issues such as context window length, inference cost, and format hallucination. We present Dripper, an efficient HTML main content extraction framework powered by lightweight language models, which addresses these challenges through four key innovations: (1) We design a specialized HTML simplification algorithm that reduces input token count to 22% compared to raw HTML while preserving critical structural information; (2) We reformulate main content extraction as a semantic block sequence classification task, significantly reducing inference cost; (3) We introduce a controlled decoding mechanism that strictly constrains the output space through logits processors, effectively eliminating hallucination issues common in small-scale models; (4) We propose WebMainBench, an evaluation dataset containing over 7,800 web pages with meticulously human-annotated main content extraction labels. Experimental results demonstrate that using only a 0.6B parameter model, Dripper achieves state-of-the-art performance across all evaluation benchmarks and outperforms all baseline methods, attaining an ROUGE-N F1 score of 81.58%( 83.13% with fall-back strategy) on our proposed WebMainBench dataset.

Czech name

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Czech description

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Type

O - Miscellaneous

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

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Continuities

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Publication year

2025

Confidentiality

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