@article{olsen-etal-2026-muc,
    title = "{MUC}-4 Revisited: Document-level Event Analysis beyond Span-based Arguments",
    author = "Olsen, Helene B{\o}sei  and
      Velldal, Erik  and
      {\O}vrelid, Lilja",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    journal = "International Conference on Language Resources and Evaluation",
    volume = "main",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.617/",
    pages = "7766--7780",
    abstract = "Automatically predicting structured representations of events has long been a central goal in information extraction, yet most contemporary work remains limited to identifying contiguous text spans as event arguments. This span-centric formulation fails to capture higher-level aspects of real-world events, such as actor identities, temporal scope, and aggregated outcomes, that many event-centred applications depend on. While commonly treated as a standard extractive benchmark, MUC-4 originally combined span-based arguments with normalised, inferred, and categorical fields, reflecting a richer, application-driven design. In this paper, we revisit MUC-4 in its full original formulation, casting it as an abstractive event analysis task that connect traditional event extraction goals with modern generative and document-level paradigms. We provide the first systematic evaluation of fine-tuned generative models in this extended formulation on MUC-4, examining how post-training stages and model size affect performance across both span-based and higher-level, semantically grounded event information. An extensive error analysis highlights practical challenges and directions for future work."
}