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arXiv · 2601.18788

Unsupervised Text Segmentation via Kernel Change-Point Detection on Sentence Embeddings

Mumin Jia·Jairo Diaz-Rodriguez
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Abstract

Unsupervised text segmentation is crucial because boundary labels are expensive, subjective, and often fail to transfer across domains and granularity choices. We propose Embed-KCPD, a training-free method that represents sentences as embedding vectors and estimates boundaries by minimizing a penalized KCPD objective. Beyond the algorithmic instantiation, we develop, to our knowledge, the first dependence-aware theory for KCPD under $m$-dependent sequences, a finite-memory abstraction of short-range dependence common in language. We prove an oracle inequality for the population penalized risk and a localization guarantee showing that each true change point is recovered within a window that is small relative to segment length. To connect theory to practice, we introduce an LLM-based simulation framework that generates synthetic documents with controlled finite-memory dependence and known boundaries, validating the predicted scaling behavior. Across standard segmentation benchmarks, Embed-KCPD often outperforms strong unsupervised baselines. A case study on Taylor Swift's tweets illustrates that Embed-KCPD combines strong theoretical guarantees, simulated reliability, and practical effectiveness for text segmentation.

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Mumin Jia, Jairo Diaz-Rodriguez. 2026-01-26. Unsupervised Text Segmentation via Kernel Change-Point Detection on Sentence Embeddings. https://arxiv.org/abs/2601.18788

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