Boundary Matters: Leveraging Structured Text Plots for Long Text Outline Generation (2025)

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.

## introduction well-crafted stories typically consist of multiple semantically coherent chapters, each of which revolves around a particular theme. an good outline concisely captures the structural content of a story, offering clear guidance for navigation and significantly easing the cognitive load required to comprehend the entire text. furthermore, it reveals the underlying thematic structure of the text @xcite @xcite . prior research on outline generation @xcite has predominantly focused on short-to-medium texts (50-1,000 tokens) * *corresponding author such as news articles and public notices, employing paragraph-level segmentation strategies to assist rapid content structure comprehension. while effective in domains like socio-economic analysis @xcite , these methods face critical challenges when processing fictional narratives exceeding 10,000 tokens (e.g., greek mythology cycles, or novels like fights break sphere and the count of monte cristo). the extended narrative scope introduces multi-layered semantic dependencies and nonlinear thematic progression @xcite , complicating traditional outline extraction. although fictional works primarily serve entertainment purposes, their complex narrative architectures often encode subcultural paradigms and historical zeitgeist-exemplified by camel xiangzi's critique of social stratification and fights break sphere's exploration of agency versus determinism. automated outline generation (og) for such texts provides dual benefits: enabling efficient plot navigation for general readers through hierarchical event graphs, and creating structured corpora for computational humanities research across history, media psychology, and cultural studies @xcite . when dealing with long texts in the realm of fiction, the focus shifts from basic relationships like birthplace or spouse to the occurrence of specific plot events. including alliances, enemies, clan members, betrayals, and vendetta @xcite . upon further analysis of the og challenge, we observe that it involves two structured prediction tasks: 1) identifying chapter features and plot boundaries, and 2) generating chapter summaries. these two tasks correspond to predicting the hierarchical relationships between chapters and summarizing individual chapters. while the second task can be well-handled by existing llm, particularly for shorter and medium-length texts. however, llms often exhibit inaccuracies and increased hallucinations when applied to longer texts figure 1 : a demonstration of the process of generating long text outlines. @xcite . for example, when the llm is asked to summarize a work of more than one million words, it often ignores some important plots or produces a wrong division of plots, resulting in readers unable to understand the full text. as shown in fig. 1 . another popular solution approach -fine-tuning -does not solve this problem very well either @xcite . the main reason lies in the catastrophic forgetting that may occur when fine-tuning the model, and also leads to excessive computational costs. therefore, we explore whether an ideal og framework could lightweight extend the strengths of llm to long texts. and the key challenge of this framework is to address the tasks 1). in this paper, we propose a novel end-to-end architecture, leveraging structured text plots for outlines (lestrtp), to address this challenge. the core idea is to enhance llm outputs by enriching them with plot boundary information, which is first determined by a neural network and then used to guide the llm in generating a detailed outline. we find that graph more effectively captures relationships between entities within chapters, thereby better reflecting chapter characteristics. therefore, we begin by constructing the individual chapters into graphs. our method first generates entity nodes using a chapter-level graph generation module and constructs an adjacency matrix between nodes based on syntactic dependency relationships. for every node feature vectors, we select entity word vectors and expand the feature set to include the tf-idf matrix of entities while incorporating chapter numbers to represent contextual coherence. we then apply an enhanced graph neural network utilizing gat @xcite to learn from the chapter graph. additionally, we design a search operator that uses properties of markov chains to determine plot boundaries based on the potential distance between chapter embeddings. finally, the theme and summary of each plot are generated by the llm, which are then combined to form the overall outline. to facilitate research, we have constructed a new chinese benchmark dataset, named novoutline-set. the entire dataset contains ultra-long texts of 28 different topics. the average number of chapters is 580.8, and the maximum number of chapters in a single text is 2271. experimental results demonstrate that our proposed method significantly outperforms all baselines in plot segmentation accuracy, with an average improvement of 45.2%. we also performed a detailed diversity and fluency analysis of the generated outlines, which showed a minimum performance improvement of 12.5% and 37.7% respectively. this demonstrates that our model can better understand the structure of the learned content. the contributions of our work are summarized as follows: ## method the long text og task is defined as follows: given a long text @xmath0, c 2 , ..., c n }, @xmath1, where c i denotes a chapter, we formulate the task as identifying plot boundaries i @xmath2, 1} between chapter pairs (c i , c j ). the segmentation results are entered into the llm to obtain an accurate text outline. in the transformation of the plot, there must be changes in scenes or the evolution of the relationship between characters and things accompanying it @xcite . this variation corresponds to the frequency, or affiliation, of characters or objects appearing in each chapter. therefore, by constructing chapter graph to explore relationship and achieve the separation of chapter features in the inner product space, the plot boundaries can be well distinguished. the detailed framework is illustrated in fig. 2 . ## conclusion in this paper, we propose a method based on plot segmentation to guide llm in generating better outlines for long texts. first, the chapter-level graph effectively captures chapter feature information. based on the chapter embeddings learned by the gat, we divide plot boundaries use im-proved markov chain properties. finally, llm generates a complete outline based on precise plot boundaries. comparative experiments demonstrate that our method not only enhances the accuracy of outline generation but also improves fluency and diversity. this approach addresses a gap in chinese language research, and aids readers in quickly understanding the content of ultra-long texts, facilitates scholars across various fields in conducting relevant humanistic research. the future research direction is to further explore how to solve the effects of hyperparameters, so as to reduce expert intervention.