# Attention-based Neural Text Segmentation

arXiv: https://arxiv.org/abs/1808.09935 · HTML: https://mlsanity.com/paper/1808.09935/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2018.

## Built on by (most cited first, 26 of 26)

- [Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review](https://mlsanity.com/paper/2107.02975/) (2021)
- [Unsupervised Topic Segmentation of Meetings with BERT Embeddings](https://mlsanity.com/paper/2106.12978/) (2021)
- [Improving Unsupervised Dialogue Topic Segmentation with Utterance-Pair Coherence Scoring](https://mlsanity.com/paper/2106.06719/) (2021)
- [Improving Long Document Topic Segmentation Models With Enhanced Coherence Modeling](https://mlsanity.com/paper/2310.11772/) (2023)
- [Sequence Model with Self-Adaptive Sliding Window for Efficient Spoken Document Segmentation](https://mlsanity.com/paper/2107.09278/) (2021)
- [Multi-Granularity Prompts for Topic Shift Detection in Dialogue](https://mlsanity.com/paper/2305.14006/) (2023)
- [Recent Trends in Linear Text Segmentation: a Survey](https://mlsanity.com/paper/2411.16613/) (2024)
- [Topic Shift Detection in Chinese Dialogues: Corpus and Benchmark](https://mlsanity.com/paper/2305.01195/) (2023)
- [Topic Segmentation Model Focusing on Local Context](https://mlsanity.com/paper/2301.01935/) (2023)
- [Advancing Topic Segmentation and Outline Generation in Chinese Texts: The Paragraph-level Topic Representation, Corpus, and Benchmark](https://mlsanity.com/paper/2305.14790/) (2023)
- [Ascle: A Python Natural Language Processing Toolkit for Medical Text Generation](https://mlsanity.com/paper/2311.16588/) (2023)
- [PREME: Preference-based Meeting Exploration through an Interactive Questionnaire](https://mlsanity.com/paper/2205.02370/) (2022)
- [EHRKit: A Python Natural Language Processing Toolkit for Electronic Health Record Texts](https://mlsanity.com/paper/2204.06604/) (2022)
- [Advancing Topic Segmentation of Broadcasted Speech with Multilingual Semantic Embeddings](https://mlsanity.com/paper/2409.06222/) (2024)
- [Topic Segmentation Using Generative Language Models](https://mlsanity.com/paper/2601.03276/) (2025)
- [Unsupervised Text Segmentation via Kernel Change-Point Detection on Sentence Embeddings](https://mlsanity.com/paper/2601.18788/) (2026)
- [Identifying Introductions in Podcast Episodes from Automatically Generated Transcripts](https://mlsanity.com/paper/2110.07096/) (2021)
- [Unsupervised Scientific Abstract Segmentation with Normalized Mutual Information](https://mlsanity.com/paper/2305.11553/) (2023)
- [Topic-driven Distant Supervision Framework for Macro-level Discourse Parsing](https://mlsanity.com/paper/2305.13755/) (2023)
- [Grounded Complex Task Segmentation for Conversational Assistants](https://mlsanity.com/paper/2309.11271/) (2023)
- [BP-Seg: A graphical model approach to unsupervised and non-contiguous text segmentation using belief propagation](https://mlsanity.com/paper/2505.16965/) (2025)
- [Consistent Kernel Change-Point Detection under m-Dependence for Text Segmentation](https://mlsanity.com/paper/2510.03437/) (2025)
- [Chunking Strategies for Multimodal AI Systems](https://mlsanity.com/paper/2512.00185/) (2025)
- [SegNSP: Revisiting Next Sentence Prediction for Linear Text Segmentation](https://mlsanity.com/paper/2601.03474/) (2026)
- [Toward General Semantic Chunking: A Discriminative Framework for Ultra-Long Documents](https://mlsanity.com/paper/2602.23370/) (2025)
- [CobSeg: Coherence Boundary Modeling for Dialogue Topic Segmentation](https://mlsanity.com/paper/2605.30668/) (2026)

## Abstract (arXiv)

Text segmentation plays an important role in various Natural Language Processing (NLP) tasks like summarization, context understanding, document indexing and document noise removal. Previous methods for this task require manual feature engineering, huge memory requirements and large execution times. To the best of our knowledge, this paper is the first one to present a novel supervised neural approach for text segmentation. Specifically, we propose an attention-based bidirectional LSTM model where sentence embeddings are learned using CNNs and the segments are predicted based on contextual information. This model can automatically handle variable sized context information. Compared to the existing competitive baselines, the proposed model shows a performance improvement of ~7% in WinDiff score on three benchmark datasets.