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.

翻译：本文提出了一种新颖且实用的叙事理解范式，源于对叙事文中各段落之间往往存在连贯关系而非孤立存在的观察。为此，我们构建了一种名为NARCO的叙事文本图结构，该图刻画了整体语境中与任务无关的连贯性依赖关系。特别地，NARCO中的边蕴含两个语境片段之间的回溯式自由形式问题，反映了高层级连贯关系，这一设计灵感源于人类不断从先前语境中回溯相关事件的认知感知机制。重要的是，该图通过我们设计的两阶段大语言模型提示方法实例化实现，无需依赖人工标注。本文从三个独特维度考察其实际效用，包括：通过概括识别检验边的有效性、通过情节检索实现局部语境增强，以及以长文档问答为例的广泛适用性研究。实验表明，基于NARCO的方法在上述三项任务中均取得了性能提升。