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Our survey examines how natural language processing (NLP) research uses LLM methods to engage with diverse concepts from narrative studies. We use established distinctions from narratology to categorise ongoing efforts and discover the following: \\redtext{(a) narrative texts come from diverse sources beyond just literature, (b) theoretical synthesis and validation are potential outcomes, (c) generation tasks lag behind understanding in several ways: theoretical application, post-training methods, exploring non-fiction narratives and addressing narrative levels beyond fabula and discourse.} For future directions, instead of the pursuit of a single, generalised benchmark for `narrative quality', we believe that progress can benefit from efforts that focus on the following: defining","title":"Narrative Theory-Driven LLM Methods for Automatic Story Generation and Understanding: A Survey","url":"https://arxiv.org/abs/2602.15851","vendor":"arxiv_cs_ai"},"summary":"arXiv:2602.15851v2 Announce Type: replace-cross \nAbstract: Applications of narrative theories using large language models (LLMs) deliver promising methods in automatic story generation and understanding tasks. 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