This research introduces a novel structure-aware generative model that enhances biomedical event extraction by better capturing complex relationships in medical texts.

• Transforms traditional classification-based extraction into a sequence generation problem
• Leverages the power of large language models for biomedical text understanding
• Incorporates cross-instance information that previous generative approaches overlooked
• Achieves improved performance on key biomedical benchmarks (MLEE, GE11, PHEE)

This advancement matters for healthcare and life sciences by enabling more accurate extraction of complex biomedical relationships from scientific literature, potentially accelerating research insights and medical discoveries.

A Structure-aware Generative Model for Biomedical Event Extraction