This work introduces a novel and practical paradigm for narrative comprehension, stemming from the observation that individual passages within narratives are often cohesively related than being isolated. We therefore propose to formulate a graph upon narratives dubbed NARCO that depicts a task-agnostic coherence dependency of the entire context. Especially, edges in NARCO encompass retrospective free-form questions between two context snippets reflecting high-level coherent relations, inspired by the cognitive perception of humans who constantly reinstate relevant events from prior context. Importantly, our graph is instantiated through our designed two-stage LLM prompting, thereby without reliance on human annotations. We present three unique studies on its practical utility, examining the edge efficacy via recap identification, local context augmentation via plot retrieval, and broader applications exemplified by long document QA. Experiments suggest that our approaches leveraging NARCO yield performance boost across all three tasks.

通过利用NARCO图，在叙事理解中提供了一种新颖且实用的范式，其通过形成图表并描绘整个上下文的任务无关的连贯依赖关系，从而实现了回顾性自由形式问题、局部上下文扩充和广泛的应用。实验表明，利用NARCO的方法在所有三个任务中均提高了性能。

叙事背景的图表示：通过回顾性问题实现连贯依赖