# Unsupervised Topic Segmentation of Meetings with BERT Embeddings

arXiv: https://arxiv.org/abs/2106.12978 · HTML: https://mlsanity.com/paper/2106.12978/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 35 references that are in the map.
- First posted on arXiv 24 Jun 2021.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books](https://mlsanity.com/paper/1506.06724/) (2015)
- [SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation](https://mlsanity.com/paper/1708.00055/) (2017)
- [SECTOR: A Neural Model for Coherent Topic Segmentation and Classification](https://mlsanity.com/paper/1902.04793/) (2019)
- [Attention-based Neural Text Segmentation](https://mlsanity.com/paper/1808.09935/) (2018)

## Built on by (most cited first, 30 of 30)

- [Uncovering the Potential of ChatGPT for Discourse Analysis in Dialogue: An Empirical Study](https://mlsanity.com/paper/2305.08391/) (2023)
- [Improving Long Document Topic Segmentation Models With Enhanced Coherence Modeling](https://mlsanity.com/paper/2310.11772/) (2023)
- [Unsupervised Dialogue Topic Segmentation with Topic-aware Utterance Representation](https://mlsanity.com/paper/2305.02747/) (2023)
- [Structured Summarization: Unified Text Segmentation and Segment Labeling as a Generation Task](https://mlsanity.com/paper/2209.13759/) (2022)
- [Recent Trends in Linear Text Segmentation: a Survey](https://mlsanity.com/paper/2411.16613/) (2024)
- [Chunk Twice, Embed Once: A Systematic Study of Segmentation and Representation Trade-offs in Chemistry-Aware Retrieval-Augmented Generation](https://mlsanity.com/paper/2506.17277/) (2025)
- [Improving Topic Segmentation by Injecting Discourse Dependencies](https://mlsanity.com/paper/2209.08626/) (2022)
- [Topic Segmentation in the Wild: Towards Segmentation of Semi-structured & Unstructured Chats](https://mlsanity.com/paper/2211.14954/) (2022)
- [Unsupervised Dialogue Topic Segmentation in Hyperdimensional Space](https://mlsanity.com/paper/2308.10464/) (2023)
- [Towards Multi-Level Transcript Segmentation: LoRA Fine-Tuning for Table-of-Contents Generation](https://mlsanity.com/paper/2601.02128/) (2026)
- [Document Summarization with Text Segmentation](https://mlsanity.com/paper/2301.08817/) (2023)
- [Unsupervised Mutual Learning of Discourse Parsing and Topic Segmentation in Dialogue](https://mlsanity.com/paper/2405.19799/) (2024)
- [TocBERT: Medical Document Structure Extraction Using Bidirectional Transformers](https://mlsanity.com/paper/2406.19526/) (2024)
- [A Novel LLM-based Two-stage Summarization Approach for Long Dialogues](https://mlsanity.com/paper/2410.06520/) (2024)
- [PREME: Preference-based Meeting Exploration through an Interactive Questionnaire](https://mlsanity.com/paper/2205.02370/) (2022)
- [Action-Item-Driven Summarization of Long Meeting Transcripts](https://mlsanity.com/paper/2312.17581/) (2023)
- [Topic Segmentation of Semi-Structured and Unstructured Conversational Datasets using Language Models](https://mlsanity.com/paper/2310.17120/) (2023)
- [Multimodal Fusion and Coherence Modeling for Video Topic Segmentation](https://mlsanity.com/paper/2408.00365/) (2024)
- [Unsupervised Text Segmentation via Kernel Change-Point Detection on Sentence Embeddings](https://mlsanity.com/paper/2601.18788/) (2026)
- [Reading Between the Waves: Robust Topic Segmentation Using Inter-Sentence Audio Features](https://mlsanity.com/paper/2602.06647/) (2026)
- [Unsupervised Scientific Abstract Segmentation with Normalized Mutual Information](https://mlsanity.com/paper/2305.11553/) (2023)
- [GPT-Calls: Enhancing Call Segmentation and Tagging by Generating Synthetic Conversations via Large Language Models](https://mlsanity.com/paper/2306.07941/) (2023)
- [Grounded Complex Task Segmentation for Conversational Assistants](https://mlsanity.com/paper/2309.11271/) (2023)
- [Semantic Source Code Segmentation using Small and Large Language Models](https://mlsanity.com/paper/2507.08992/) (2025)
- [Consistent Kernel Change-Point Detection under m-Dependence for Text Segmentation](https://mlsanity.com/paper/2510.03437/) (2025)
- [SegNSP: Revisiting Next Sentence Prediction for Linear Text Segmentation](https://mlsanity.com/paper/2601.03474/) (2026)
- [Rethinking Meeting Effectiveness: A Benchmark and Framework for Temporal Fine-grained Automatic Meeting Effectiveness Evaluation](https://mlsanity.com/paper/2604.17260/) (2026)
- [CobSeg: Coherence Boundary Modeling for Dialogue Topic Segmentation](https://mlsanity.com/paper/2605.30668/) (2026)
- [Dialogue Summarization with Emotion Dynamics Using Topic- and Participant-Centric Decomposition](https://mlsanity.com/paper/2607.14769/) (2026)
- [Zero-Shot Cue-Grounded Topic Segmentation of Spoken Documents](https://mlsanity.com/paper/2609.34425/) (2026)

## Abstract (arXiv)

Topic segmentation of meetings is the task of dividing multi-person meeting transcripts into topic blocks. Supervised approaches to the problem have proven intractable due to the difficulties in collecting and accurately annotating large datasets. In this paper we show how previous unsupervised topic segmentation methods can be improved using pre-trained neural architectures. We introduce an unsupervised approach based on BERT embeddings that achieves a 15.5% reduction in error rate over existing unsupervised approaches applied to two popular datasets for meeting transcripts.