Topic-based document segmentation with probabilistic latent semantic analysis
Proceedings of the eleventh international conference on Information and knowledge management
A critique and improvement of an evaluation metric for text segmentation
Computational Linguistics
Discourse Segmentation in Aid of Document Summarization
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 3 - Volume 3
Domain-independent text segmentation using anisotropic diffusion and dynamic programming
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Advances in domain independent linear text segmentation
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Optimal multi-paragraph text segmentation by dynamic programming
ACL '98 Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics - Volume 2
Multi-paragraph segmentation of expository text
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
An automatic method of finding topic boundaries
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
A Dynamic Programming Algorithm for Linear Text Segmentation
Journal of Intelligent Information Systems
A statistical model for domain-independent text segmentation
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Minimum cut model for spoken lecture segmentation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Using multiple discriminant analysis approach for linear text segmentation
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
An iterative approach to text segmentation
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
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Text segmentation has a wide range of applications such as information retrieval, question answering and text summarization. In recent years, the use of semantics has been proven to be effective in improving the performance of text segmentation. Particularly, in finding the subtopic boundaries, there have been efforts in focusing on either maximizing the lexical similarity within a segment or minimizing the similarity between adjacent segments. However, no optimal solutions have been attempted to simultaneously achieve maximum within-segment similarity and minimum between-segment similarity. In this paper, a domain independent model based on min-max similarity (MMS) is proposed in order to fill the void. Dynamic programming is adopted to achieve global optimization of the segmentation criterion function. Comparative experimental results on real corpus have shown that MMS model outperforms previous segmentation approaches.