Event extraction is essential for event understanding and analysis. It supports tasks such as document summarization and decision-making in emergency scenarios. However, existing event extraction approaches have limitations: (1) closed-domain algorithms are restricted to predefined event types and thus rarely generalize to unseen types and (2) open-domain event extraction algorithms, capable of handling unconstrained event types, have largely overlooked the potential of large language models (LLMs) despite their advanced abilities. Additionally, they do not explicitly model document-level contextual, structural, and semantic reasoning, which are crucial for effective event extraction but remain challenging for LLMs due to lost-in-the-middle phenomenon and attention dilution. To address these limitations, we propose multimodal open-domain event extraction, MODEE , a novel approach for open-domain event extraction that combines graph-based learning with text-based representation from LLMs to model document-level reasoning. Empirical evaluations on large datasets demonstrate that MODEE outperforms state-of-the-art open-domain event extraction approaches and can be generalized to closed-domain event extraction, where it outperforms existing algorithms.

翻译：事件抽取对于事件理解与分析至关重要，它支持文档摘要和紧急场景下的决策制定等任务。然而，现有的事件抽取方法存在局限性：（1）封闭域算法局限于预定义的事件类型，因此难以泛化到未见过的类型；（2）开放域事件抽取算法虽能处理不受约束的事件类型，但在很大程度上忽略了大型语言模型（LLMs）的先进能力。此外，这些算法未能显式建模文档级别的上下文、结构和语义推理——这些对于有效的事件抽取至关重要，但由于中间信息丢失和注意力稀释现象，对LLMs而言仍具有挑战性。为解决这些局限性，我们提出多模态开放域事件抽取方法MODEE，这是一种结合基于图的LLMs文本表示学习的新颖开放域事件抽取方法，用于建模文档级推理。在大型数据集上的实证评估表明，MODEE优于最先进的开放域事件抽取方法，并且可以泛化到封闭域事件抽取场景，在此场景中其性能优于现有算法。