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    <abstract>Outline generation aims to uncover the internal content structure of a document by identifying potential chapter connections and generating corresponding summaries. A robust outline generation model strives for coherence between and within plots. However, existing methods perform well on short- and medium-length texts and struggle with generating readable outlines for very long texts (e.g., fictional literary works). The primary challenge lies in their inability to accurately segment plots within long texts. To address this issue, we propose a novel unsupervised guidance framework, LeStrTP, to guide large language model (LLM) outline generation. This framework ensures that each structured plot encapsulates complete causality by accurately identifying plot boundaries. Specifically, the LeStrTP framework constructs chapter-level graph from long texts and learns their embeddings. Subsequently, through Markov chain modeling chapter dependence, a unique search operator is designed to achieve plot segmentation. To facilitate research on this task, we introduce a new annotated benchmark dataset, NovOutlineSet. Experimental results demonstrate that structured plots not only enhance the coherence and integrity of generated outlines but also significantly improve their quality.</abstract>
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%0 Conference Proceedings
%T Boundary Matters: Leveraging Structured Text Plots for Long Text Outline Generation
%A Ma, Yuanchi
%A Liu, Jiamou
%A He, Hui
%A Zhang, Libo
%A Li, Haoyuan
%A Niu, Zhendong
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F ma-etal-2025-boundary
%X Outline generation aims to uncover the internal content structure of a document by identifying potential chapter connections and generating corresponding summaries. A robust outline generation model strives for coherence between and within plots. However, existing methods perform well on short- and medium-length texts and struggle with generating readable outlines for very long texts (e.g., fictional literary works). The primary challenge lies in their inability to accurately segment plots within long texts. To address this issue, we propose a novel unsupervised guidance framework, LeStrTP, to guide large language model (LLM) outline generation. This framework ensures that each structured plot encapsulates complete causality by accurately identifying plot boundaries. Specifically, the LeStrTP framework constructs chapter-level graph from long texts and learns their embeddings. Subsequently, through Markov chain modeling chapter dependence, a unique search operator is designed to achieve plot segmentation. To facilitate research on this task, we introduce a new annotated benchmark dataset, NovOutlineSet. Experimental results demonstrate that structured plots not only enhance the coherence and integrity of generated outlines but also significantly improve their quality.
%U https://aclanthology.org/2025.findings-emnlp.4/
%P 49-63

Markdown (Informal)

[Boundary Matters: Leveraging Structured Text Plots for Long Text Outline Generation](https://aclanthology.org/2025.findings-emnlp.4/) (Ma et al., Findings 2025)

• Boundary Matters: Leveraging Structured Text Plots for Long Text Outline Generation (Ma et al., Findings 2025)

ACL

• Yuanchi Ma, Jiamou Liu, Hui He, Libo Zhang, Haoyuan Li, and Zhendong Niu. 2025. Boundary Matters: Leveraging Structured Text Plots for Long Text Outline Generation. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 49–63, Suzhou, China. Association for Computational Linguistics.