In this paper, we introduce and tackle the Outline Generation (OG) task, which aims to unveil the inherent content structure of a multi-paragraph document by identifying its potential sections and generating the corresponding section headings. Without loss of generality, the OG task can be viewed as a novel structured summarization task. To generate a sound outline, an ideal OG model should be able to capture three levels of coherence, namely the coherence between context paragraphs, that between a section and its heading, and that between context headings. The first one is the foundation for section identification, while the latter two are critical for consistent heading generation. In this work, we formulate the OG task as a hierarchical structured prediction problem, i.e., to first predict a sequence of section boundaries and then a sequence of section headings accordingly. We propose a novel hierarchical structured neural generation model, named HiStGen, for the task. Our model attempts to capture the three-level coherence via the following ways. First, we introduce a Markov paragraph dependency mechanism between context paragraphs for section identification. Second, we employ a section-aware attention mechanism to ensure the semantic coherence between a section and its heading. Finally, we leverage a Markov heading dependency mechanism and a review mechanism between context headings to improve the consistency and eliminate duplication between section headings. Besides, we build a novel WIKIOG dataset, a public collection which consists of over 1.75 million document-outline pairs for research on the OG task. Experimental results on our benchmark dataset demonstrate that our model can significantly outperform several state-of-the-art sequential generation models for the OG task.

本研究提出并解决了概述生成 (OG) 任务，该任务旨在通过识别多段文档的潜在部分并生成相应的部分标题来揭示其内在内容结构。我们将OG任务作为一种新颖的结构化摘要任务来处理，在模型中通过引入马尔科夫段落依赖机制、区域感知注视机制、马尔科夫标题依赖机制以及评论机制等方法来捕捉三个层次的一致性，即上下文段落之间的一致性、一个部分和它的标题之间的一致性以及上下文标题之间的一致性。实验结果表明，我们的模型可以显著优于OG任务的几种最先进的顺序生成模型。

大纲生成：理解文档中内在的内容结构