This paper describes a method for linear text segmentation that is more accurate or at least as accurate as state-of-the-art methods (Utiyama and Isahara, 200

Choi, Freddy Y. Y.

Moore, Johanna

Wiemer-Hastings, Peter

This paper describes a method for linear text segmentation that is more accurate or at least as accurate as state-of-the-art methods (Utiyama and Isahara, 2001; Choi, 2000a). Inter-sentence similarity is estimated by latent semantic analysis (LSA). Boundary locations are discovered by divisive clustering. Test results show LSA is a more accurate similarity measure than the cosine metric (van Rijsbergen, 1979)

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Latent Semantic Analysis for Text Segmentation

https://www.pure.ed.ac.uk/ws/files/18572356/Choi_Wiemer_Hastings_ET_AL_2001_Latent_Semantic_Analysis_for_Text_Segmentation.pdf

Latent Semantic Analysis for Text Segmentation

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