Graph Representation of Narrative Context: Coherence Dependency via Retrospective Questions

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.