# Outline Generation: Understanding the Inherent Content Structure of Documents

arXiv: https://arxiv.org/abs/1905.10039 · HTML: https://mlsanity.com/paper/1905.10039/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2019.

## Built on by (most cited first, 12 of 12)

- [End-to-End Segmentation-based News Summarization](https://mlsanity.com/paper/2110.07850/) (2021)
- [From Text Segmentation to Smart Chaptering: A Novel Benchmark for Structuring Video Transcriptions](https://mlsanity.com/paper/2402.17633/) (2024)
- [PODTILE: Facilitating Podcast Episode Browsing with Auto-generated Chapters](https://mlsanity.com/paper/2410.16148/) (2024)
- [Structured Summarization: Unified Text Segmentation and Segment Labeling as a Generation Task](https://mlsanity.com/paper/2209.13759/) (2022)
- [Advancing Topic Segmentation and Outline Generation in Chinese Texts: The Paragraph-level Topic Representation, Corpus, and Benchmark](https://mlsanity.com/paper/2305.14790/) (2023)
- [Beyond Transcripts: A Renewed Perspective on Audio Chaptering](https://mlsanity.com/paper/2602.08979/) (2026)
- [Visual Subtitle Feature Enhanced Video Outline Generation](https://mlsanity.com/paper/2208.11307/) (2022)
- [Explainability of Text Processing and Retrieval Methods: A Survey](https://mlsanity.com/paper/2212.07126/) (2022)
- [Towards Zero-Shot Personalized Table-to-Text Generation with Contrastive Persona Distillation](https://mlsanity.com/paper/2304.08911/) (2023)
- [fLSA: Learning Semantic Structures in Document Collections Using Foundation Models](https://mlsanity.com/paper/2410.05481/) (2024)
- [Long text outline generation: Chinese text outline based on unsupervised framework and large language mode](https://mlsanity.com/paper/2412.00810/) (2024)
- [Writing Like the Best: Exemplar-Based Expository Text Generation](https://mlsanity.com/paper/2505.18859/) (2025)

## Abstract (arXiv)

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.