  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.
