EXCEEDS: New Framework and Dataset for Event Extraction in Scientific Documents
Researchers have proposed EXCEEDS, an end-to-end event extraction framework for scientific text, alongside SciEvents, a large-scale dataset of 2,508 documents and 24,381 annotated events. Scientific documents present unique challenges due to denser event nuggets and more complex information structures compared to news or finance domains. The work, accepted for oral presentation at ACL 2026, addresses a gap in NLP tooling for scientific literature and is released publicly to support further research.
The paper introduces two contributions aimed at improving automated understanding of scientific literature through event extraction. First, the authors construct SciEvents, a document-level dataset containing 2,508 scientific documents and 24,381 events, built using a domain-tailored schema and multi-stage manual annotation with quality control. Second, they propose EXCEEDS (Extracting Complex Events via Nugget-based Grid Modeling in Scientific Domain), an end-to-end framework that encodes dense event nuggets into a grid matrix, recasting complex event extraction as a structured grid modeling task. The authors argue that existing event extraction research—primarily focused on news, finance, and biology—does not adequately address the higher nugget density and more complex information forms found in scientific text. Experiments on SciEvents show that EXCEEDS achieves state-of-the-art performance. Both the dataset and framework are publicly released, and the work has been accepted as an oral presentation at the ACL 2026 Main Conference.
What's missing
The abstract does not specify which scientific subdomains or disciplines are covered by SciEvents, nor does it detail inter-annotator agreement metrics or discuss how well EXCEEDS generalizes to scientific domains outside the training distribution.
What different sources said
- arXiv cs.CLCenter
EXCEEDS: Extracting Complex Events via Nugget-based Grid Modeling in Scientific Domain
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