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

Cascading Large Language Models for Salient Event Graph Generation

Story generation is a task that aims to automatically produce multiple
sentences to make up a meaningful story. This task is challenging because it
requires high-level understanding of semantic meaning of sentences and
causality of story events. Naive sequence-to-sequence models generally fail to
acquire such knowledge, as the logical correctness can hardly be guaranteed in
a text generation model without the strategic planning. In this paper, we focus
on planning a sequence of events assisted by event graphs, and use the events
to guide the generator. Instead of using a sequence-to-sequence model to output
a storyline as in some existing works, we propose to generate an event sequence
by walking on an event graph. The event graphs are built automatically based on
the corpus. To evaluate the proposed approach, we conduct human evaluation both
on event planning and story generation. Based on large-scale human annotation
results, our proposed approach is shown to produce more logically correct event
sequences and stories.

通过事件图谱，本论文提出构建事件序列以引导生成器的方法来生成故事，实现故事的高水平理解和语义理解，从而产生更多逻辑上正确的事件序列和故事。