@inproceedings{li-etal-2026-beyond,
    title = "Beyond a Single Extractor: Re-thinking {HTML}-to-Text Extraction for {LLM} Pre-training",
    author = "Li, Jeffrey  and
      Gardner, Joshua P  and
      Kang, Doug  and
      Shi, Fangping  and
      Singh, Karanjeet  and
      Li, Chun-Liang  and
      Shandilya, Herumb  and
      Hall, David Leo Wright  and
      Tuzel, Oncel  and
      Liang, Percy  and
      Schmidt, Ludwig  and
      Pouransari, Hadi  and
      Faghri, Fartash",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.307/",
    pages = "5836--5861",
    ISBN = "979-8-89176-386-9",
    abstract = "One of the first pre-processing steps for constructing web-scale LLM pretraining datasets involves extracting text from HTML. Despite the immense diversity of web content, existing open-source datasets predominantly apply a single fixed extractor to all webpages. In this work, we investigate whether this practice leads to suboptimal coverage and utilization of Internet data. We first show that while different extractors may lead to similar model performance on standard language understanding tasks, the pages surviving a fixed filtering pipeline can differ substantially. This suggests a simple intervention: by taking a Union over different extractors, we can increase the token yield of DCLM-Baseline by up to 71{\%} while maintaining benchmark performance. We further show that for structured content such as tables and code blocks, extractor choice can significantly impact downstream task performance, with differences of up to 10 percentage points (p.p.) on WikiTQ and 3 p.p. on HumanEval."
}