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. Fine-tuning contextualised graph generation models on the LLM-generated graphs outperforms the models trained on CAEVO-generated data. Experimental results on a human-annotated test set show that the proposed method generates salient and more accurate graphs, outperforming competitive baselines.

本文介绍了CALLMSAE，这是一个基于CAscading Large Language Model框架的关键事件图生成方法，它利用LLMs的能力，无需昂贵的人工注释。实验结果表明，该方法生成的关键事件图更准确，胜过竞争性基线模型。

用于显著性事件图生成的级联大型语言模型