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Open Access

ARTICLE

Chronological Passage Assembly for Retrieval-Augmented Generation in Narrative Question Answering

Byeongjeong Kim, Jeonghyun Park, Joonho Yang, Hwanhee Lee*
Department of Artificial Intelligence, Chung-Ang University, Seoul, Republic of Korea
* Corresponding Author: Hwanhee Lee. Email: email
(This article belongs to the Special Issue: Generative Artificial Intelligence and Large Language Models: Methods, Architectures, and Applications)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.082460

Received 16 March 2026; Accepted 04 June 2026; Published online 06 July 2026

Abstract

Long-context question answering over narrative documents remains challenging because many questions require reconstructing event sequences while preserving local contextual flow under limited context budgets. Existing retrieval-augmented generation (RAG) methods typically retrieve document snippets independently, which can fragment narratives and harm temporal dependencies. We propose ChronoRAG, a retrieval framework for narrative question answering that first converts sequential document chunks into concise relation descriptions and then retrieves relevant units together with their adjacent chronological context. This design preserves retrieval precision while providing the generator with coherent local narrative structure. Experiments on NarrativeQA and GutenQA show that ChronoRAG improves performance on NarrativeQA and remains competitive on GutenQA, with particularly strong gains on questions that require chronology-sensitive context. These results suggest that explicitly modeling local event order is a useful retrieval signal for narrative question answering.

Keywords

Retrieval-augmented generation; narrative question answering; long-context reasoning; temporal reasoning; knowledge graphs

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