A Scalable Cross-Domain Event Extraction System via a Unified Generative Training Framework

Abstract:Event extraction is fundamental to information extraction. Prior approaches often separate event detection and argument extraction or depend on dataset-specific designs, limiting scalability and cross-domain generalization. We propose a unified generative sequence-to-sequence framework that performs event extraction subtasks jointly and supports both pipeline and end-to-end configurations. We fine-tune pretrained language models on multiple event datasets across diverse domains, enabling a single model to retain domain-specific semantics while generalizing over large and evolving label spaces. We demonstrate these capabilities through a web-based application tailored for researchers and practitioners. The platform supports document upload, schema-aware event extraction, visualization of triggers and arguments, and comparison of different extraction configurations across domains.

Computation and Language (cs.CL)

arXiv:2608.23261 [cs.CL]

(or arXiv:2608.23261v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.23261 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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