Cascading Large Language Models for Salient Event Graph Generation (2025)

Abstract: 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 NYT-SEG , a large-scale automatically annotated event graph dataset which can serve as distant supervision signals. Fine-tuning contextualised graph generation models on 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.

## introduction events are fundamental discourse units which form the backbone of human communication. they are interconnected through various event relations such as hierarchical, temporal, or causal relations. event relation graphs are vital for representing and understanding complex event narratives, with nodes representing events and edges denoting relationships between them. high-quality event relation graphs can enhance numerous downstream tasks, 1 code and data: https://github.com/xingwei-tan/ callmsae . after lindsay davenport defeated venus williams, 7-5, 6-4, in a match that was ho-hum until the last game, nadia petrova surprised top-seeded justine henin-hardenne, @xcite ## cascading llms to generate salient event graphs callmsae combines various prompts in a pipelined manner to generate salient event graphs. in this section, we introduce the prompts for generating salient events, and then the method for generating relation graphs based on the salient events. lastly, we define an evaluation metric for comparing event graphs: hungarian graph similarity. ## dataset in this section, we describe how we construct the nyt-seg which includes llm-generated distant training pairs and a human-annotated dataset based on the new york times (nyt) corpus.