@inproceedings{zhang-etal-2026-mmuie,
    title = "{MMUIE}: Massive Multi-Domain Universal Information Extraction for Long Documents",
    author = "Zhang, Shuyi  and
      Chen, Zhenbin  and
      Li, Shuting  and
      Tu, Kewei  and
      Jing, Li  and
      Jia, Zixia  and
      Zheng, Zilong",
    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.334/",
    pages = "6338--6370",
    ISBN = "979-8-89176-386-9",
    abstract = "We present **MMUIE**, a large-scale universal dataset for multi-domain, document-level information extraction (IE) from long texts.Existing IE systems predominantly operate at the sentence level or within narrow domains due to annotation constraints.MMUIE addresses this gap by introducing an automated annotation pipeline that integrates traditional knowledge bases with large language models to extract fine-grained entities, aliases, and relation triples across 34 domains.The dataset comprises a weakly-supervised training set and a manually verified test set, featuring 723 entity types and 456 relation types.Empirical evaluations reveal that existing sentence-level IE models and even advanced LLMs underperform on this task, highlighting the need for better domain-aware document-level models.To this end, we develop DocUIE, a universal IE model fine-tuned on MMUIE, which achieves strong generalization and transferability across domains. MMUIE lays the foundation for robust, scalable, and universal information extraction from long-form text in diverse real-world scenarios. All code, data, and models are available in https://github.com/Shuyi-zsy/Massive-Multi-Domain-UIE."
}