# Toward Fast and Accurate Neural Discourse Segmentation

arXiv: https://arxiv.org/abs/1808.09147 · HTML: https://mlsanity.com/paper/1808.09147/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2018.

## Built on by (most cited first, 19 of 19)

- [How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources](https://mlsanity.com/paper/2306.04751/) (2023)
- [Transformer over Pre-trained Transformer for Neural Text Segmentation with Enhanced Topic Coherence](https://mlsanity.com/paper/2110.07160/) (2021)
- [Deep Discourse Analysis for Generating Personalized Feedback in Intelligent Tutor Systems](https://mlsanity.com/paper/2103.07785/) (2021)
- [MHMS: Multimodal Hierarchical Multimedia Summarization](https://mlsanity.com/paper/2204.03734/) (2022)
- [ABCD: A Graph Framework to Convert Complex Sentences to a Covering Set of Simple Sentences](https://mlsanity.com/paper/2106.12027/) (2021)
- [Predicting Above-Sentence Discourse Structure using Distant Supervision from Topic Segmentation](https://mlsanity.com/paper/2112.06196/) (2021)
- [EDU-level Extractive Summarization with Varying Summary Lengths](https://mlsanity.com/paper/2210.04029/) (2022)
- [RSTGen: Imbuing Fine-Grained Interpretable Control into Long-FormText Generators](https://mlsanity.com/paper/2205.12590/) (2022)
- [Multitask Learning for Class-Imbalanced Discourse Classification](https://mlsanity.com/paper/2101.00389/) (2021)
- [AntPivot: Livestream Highlight Detection via Hierarchical Attention Mechanism](https://mlsanity.com/paper/2206.04888/) (2022)
- [Discovering Elementary Discourse Units in Textual Data Using Canonical Correlation Analysis](https://mlsanity.com/paper/2406.12997/) (2024)
- [Transfer and Active Learning for Dissonance Detection: Addressing the Rare-Class Challenge](https://mlsanity.com/paper/2305.02459/) (2023)
- [SEFORA: Student Essays with Feedback Corpus and LLM Feedback Evaluation Framework](https://mlsanity.com/paper/2607.00274/) (2026)
- [Identifying Introductions in Podcast Episodes from Automatically Generated Transcripts](https://mlsanity.com/paper/2110.07096/) (2021)
- [SNeL: A Structured Neuro-Symbolic Language for Entity-Based Multimodal Scene Understanding](https://mlsanity.com/paper/2306.06036/) (2023)
- [BP-Seg: A graphical model approach to unsupervised and non-contiguous text segmentation using belief propagation](https://mlsanity.com/paper/2505.16965/) (2025)
- [CoMuMDR: Code-mixed Multi-modal Multi-domain corpus for Discourse paRsing in conversations](https://mlsanity.com/paper/2506.08504/) (2025)
- [Improving Zero-shot Sentence Decontextualisation with Content Selection and Planning](https://mlsanity.com/paper/2509.17921/) (2025)
- [Text Summarization With Graph Attention Networks](https://mlsanity.com/paper/2604.03583/) (2026)

## Abstract (arXiv)

Discourse segmentation, which segments texts into Elementary Discourse Units, is a fundamental step in discourse analysis. Previous discourse segmenters rely on complicated hand-crafted features and are not practical in actual use. In this paper, we propose an end-to-end neural segmenter based on BiLSTM-CRF framework. To improve its accuracy, we address the problem of data insufficiency by transferring a word representation model that is trained on a large corpus. We also propose a restricted self-attention mechanism in order to capture useful information within a neighborhood. Experiments on the RST-DT corpus show that our model is significantly faster than previous methods, while achieving new state-of-the-art performance.