# Topic Segmentation and Labeling in Asynchronous Conversations

arXiv: https://arxiv.org/abs/1402.0586 · HTML: https://mlsanity.com/paper/1402.0586/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2014.

## Built on by (most cited first, 8 of 8)

- [EpiCache: Episodic KV Cache Management for Long-Term Conversation on Resource-Constrained Environments](https://mlsanity.com/paper/2509.17396/) (2025)
- [Diversity-Aware Coherence Loss for Improving Neural Topic Models](https://mlsanity.com/paper/2305.16199/) (2023)
- [Multi-turn Dialogue Comprehension from a Topic-aware Perspective](https://mlsanity.com/paper/2309.09666/) (2023)
- [Temporal Order Matters for Agentic Memory: Segment Trees for Long-Horizon Agents](https://mlsanity.com/paper/2606.04555/) (2026)
- [Learning functional sections in medical conversations: iterative pseudo-labeling and human-in-the-loop approach](https://mlsanity.com/paper/2210.02658/) (2022)
- [Topic Segmentation of Semi-Structured and Unstructured Conversational Datasets using Language Models](https://mlsanity.com/paper/2310.17120/) (2023)
- [Visualization of Unstructured Sports Data -- An Example of Cricket Short Text Commentary](https://mlsanity.com/paper/2404.00030/) (2024)
- [Semantic Source Code Segmentation using Small and Large Language Models](https://mlsanity.com/paper/2507.08992/) (2025)

## Abstract (arXiv)

Topic segmentation and labeling is often considered a prerequisite for higher-level conversation analysis and has been shown to be useful in many Natural Language Processing (NLP) applications. We present two new corpora of email and blog conversations annotated with topics, and evaluate annotator reliability for the segmentation and labeling tasks in these asynchronous conversations. We propose a complete computational framework for topic segmentation and labeling in asynchronous conversations. Our approach extends state-of-the-art methods by considering a fine-grained structure of an asynchronous conversation, along with other conversational features by applying recent graph-based methods for NLP. For topic segmentation, we propose two novel unsupervised models that exploit the fine-grained conversational structure, and a novel graph-theoretic supervised model that combines lexical, conversational and topic features. For topic labeling, we propose two novel (unsupervised) random walk models that respectively capture conversation specific clues from two different sources: the leading sentences and the fine-grained conversational structure. Empirical evaluation shows that the segmentation and the labeling performed by our best models beat the state-of-the-art, and are highly correlated with human annotations.