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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 grammaticallycoherent text, their ability to comprehend the author's thoughts remainsuncertain. This limitation hinders the practical applications of narrativeunderstanding. In this paper, we conduct a comprehensive survey of narrativeunderstanding tasks, thoroughly examining their key features, definitions,taxonomy, associated datasets, training objectives, evaluation metrics, andlimitations. Furthermore, we explore the potential of expanding thecapabilities of modularized LLMs to address novel narrative understandingtasks. By framing narrative understanding as the retrieval of the author'simaginative cues that outline the narrative structure, our study introduces afresh perspective on enhancing narrative comprehension.

Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding