Narrative understanding involves capturing the author's cognitive processes, providing insights into their knowledge, intentions, beliefs, and desires. Although large language models (LLMs) excel in generating grammatically coherent text, their ability to comprehend the author's thoughts remains uncertain. This limitation hinders the practical applications of narrative understanding. In this paper, we conduct a comprehensive survey of narrative understanding tasks, thoroughly examining their key features, definitions, taxonomy, associated datasets, training objectives, evaluation metrics, and limitations. Furthermore, we explore the potential of expanding the capabilities of modularized LLMs to address novel narrative understanding tasks. By framing narrative understanding as the retrieval of the author's imaginative cues that outline the narrative structure, our study introduces a fresh perspective on enhancing narrative comprehension.

翻译：叙事理解涉及捕捉作者的认知过程，从而洞察其知识、意图、信念和欲望。尽管大语言模型在生成语法连贯的文本方面表现出色，但其理解作者思维的能力仍不确定。这一局限阻碍了叙事理解的实际应用。本文对叙事理解任务进行了全面综述，深入探讨了其关键特征、定义、分类体系、相关数据集、训练目标、评估指标及局限性。此外，我们探索了扩展模块化大语言模型能力以处理新型叙事理解任务的潜力。通过将叙事理解定义为检索作者勾勒叙事结构的想象线索，本研究为增强叙事理解提供了全新视角。