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TextTiling: segmenting text into multi-paragraph subtopic passages
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A binary variable model for affinity propagation
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COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Bayesian unsupervised topic segmentation
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Text segmentation via topic modeling: an analytical study
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Topical segmentation: a study of human performance and a new measure of quality
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
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Proceedings of the 21st ACM international conference on Information and knowledge management
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Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
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This paper presents a new algorithm for linear text segmentation. It is an adaptation of Affinity Propagation, a state-of-the-art clustering algorithm in the framework of factor graphs. Affinity Propagation for Segmentation, or APS, receives a set of pairwise similarities between data points and produces segment boundaries and segment centres -- data points which best describe all other data points within the segment. APS iteratively passes messages in a cyclic factor graph, until convergence. Each iteration works with information on all available similarities, resulting in high-quality results. APS scales linearly for realistic segmentation tasks. We derive the algorithm from the original Affinity Propagation formulation, and evaluate its performance on topical text segmentation in comparison with two state-of-the art segmenters. The results suggest that APS performs on par with or outperforms these two very competitive baselines.