用无监督框架+大模型生成长篇中文文章提纲，提升章节分割与可读性
Long text outline generation: Chinese text outline based on unsupervised framework and large language mode
- 基于实体与句法关系构建章节图，用图注意力学深度表示
- 引入马尔可夫链算子精准划分剧情边界，解决长文本分割难题
- 适合处理小说等超长中文文本，对大模型提纲生成有显著改进
提纲生成旨在通过识别文档内在章节关系并生成章节摘要，揭示文本结构。尽管现有深度学习方法和大模型在短中等长度文本上表现良好，但在处理超长文本（如小说）时往往难以生成可读的提纲，章节分割常不连贯。本文提出一种面向中文的新型提纲生成方法，结合无监督框架与大模型。首先基于实体与句法依存关系生成章节特征图；随后，采用图注意力层构建章节图数据的深度嵌入表示；利用这些嵌入，设计基于马尔可夫链原理的算子以分割剧情边界；最后使用大模型为每个剧情段落生成摘要，形成整体提纲。我们在分割准确率与提纲可读性方面进行评估，实验结果表明，该方法在对比多个深度学习模型与大模型时表现更优。
原文摘要 · Abstract (English)
Outline generation aims to reveal the internal structure of a document by identifying underlying chapter relationships and generating corresponding chapter summaries. Although existing deep learning methods and large models perform well on small- and medium-sized texts, they struggle to produce readable outlines for very long texts (such as fictional works), often failing to segment chapters coherently. In this paper, we propose a novel outline generation method for Chinese, combining an unsupervised framework with large models. Specifically, the method first generates chapter feature graph data based on entity and syntactic dependency relationships. Then, a representation module based on graph attention layers learns deep embeddings of the chapter graph data. Using these chapter embeddings, we design an operator based on Markov chain principles to segment plot boundaries. Finally, we employ a large model to generate summaries of each plot segment and produce the overall outline. We evaluate our model based on segmentation accuracy and outline readability, and our performance outperforms several deep learning models and large models in comparative evaluations.
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