Combining graph representations with LLM embeddings enables open-domain event extraction that generalizes to unseen event types while maintaining document-level reasoning that LLMs alone struggle with.
This paper presents MODEE, a method for extracting events from documents that works with any type of event, not just predefined ones. It combines graph-based learning with large language models to better understand document structure and context, addressing limitations where LLMs struggle with long documents and lose important information in the middle.