Graph Representation of Narrative Context: Coherence Dependency via
  Retrospective Questions
Liyan Xu Jiangnan Li Mo Yu Jie Zhou
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.