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    <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.</abstract>
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%0 Conference Proceedings
%T MUC-4 Revisited: Document-level Event Analysis beyond Span-based Arguments
%A Olsen, Helene Bøsei
%A Velldal, Erik
%A Øvrelid, Lilja
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F olsen-etal-2026-muc
%X 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.
%R 10.63317/2kaxd8nu44bx
%U https://aclanthology.org/2026.lrec-1.617/
%U https://doi.org/10.63317/2kaxd8nu44bx
%P 7766-7780

Markdown (Informal)

[MUC-4 Revisited: Document-level Event Analysis beyond Span-based Arguments](https://aclanthology.org/2026.lrec-1.617/) (Olsen et al., LREC 2026)

• MUC-4 Revisited: Document-level Event Analysis beyond Span-based Arguments (Olsen et al., LREC 2026)

ACL

• Helene Bøsei Olsen, Erik Velldal, and Lilja Øvrelid. 2026. MUC-4 Revisited: Document-level Event Analysis beyond Span-based Arguments. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 7766–7780, Palma de Mallorca, Spain. ELRA Language Resource Association.