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Advances in domain independent linear text segmentation

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arxiv cs/0003083 v1 pith:3DLMKULB submitted 2000-03-30 cs.CL

classification cs.CL
keywords linearsegmentationtextaccurateadvancesboundaryclusteringcontext
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This paper describes a method for linear text segmentation which is twice as accurate and over seven times as fast as the state-of-the-art (Reynar, 1998). Inter-sentence similarity is replaced by rank in the local context. Boundary locations are discovered by divisive clustering.

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  1. Semantic Source Code Segmentation using Small and Large Language Models

    cs.SE 2025-07 conditional novelty 5.0 of 10

    Fine-tuned encoder-only models such as CodeBERT outperform zero-shot and few-shot LLMs at semantic line-level segmentation of R code, and a new annotated R dataset, StatCodeSeg, is introduced.

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