Cascading Large Language Models for Salient Event Graph Generation
This work addresses the problem of extracting structured event graphs for narrative understanding, offering an incremental improvement by focusing on salient events rather than treating all events equally.
The paper tackles the challenge of generating salient event graphs from long documents by introducing CALLMSAE, a cascading LLM framework that identifies salient events and refines event relations without human annotations, resulting in a new dataset NYT-SEG that improves graph generation accuracy over baselines.
Generating event graphs from long documents is challenging due to the inherent complexity of multiple tasks involved such as detecting events, identifying their relationships, and reconciling unstructured input with structured graphs. Recent studies typically consider all events with equal importance, failing to distinguish salient events crucial for understanding narratives. This paper presents CALLMSAE, a CAscading Large Language Model framework for SAlient Event graph generation, which leverages the capabilities of LLMs and eliminates the need for costly human annotations. We first identify salient events by prompting LLMs to generate summaries, from which salient events are identified. Next, we develop an iterative code refinement prompting strategy to generate event relation graphs, removing hallucinated relations and recovering missing edges. Powered by CALLMSAE, we present \textit{NYT-SEG}, a large-scale automatically annotated event graph dataset which can serve as distant supervision signals. Fine-tuning contextualised graph generation models on \textit{NYT-SEG} outperforms the models trained on CAEVO data. Results on a human-annotated test set show that the proposed method generates salient and more accurate graphs, outperforming competitive baselines.