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Improving unsupervised dialogue topic segmentation with utterance-pair coherence scoring[J]. arXiv preprint arXiv:2106.06719, 2021."},{"key":"e_1_3_2_1_11_1","volume-title":"Unsupervised Dialogue Topic Segmentation with Topic-aware Utterance Representation[J]. arXiv preprint arXiv:2305.02747","author":"Gao H","year":"2023","unstructured":"Gao H, Wang R, Lin T E, Unsupervised Dialogue Topic Segmentation with Topic-aware Utterance Representation[J]. arXiv preprint arXiv:2305.02747, 2023."},{"key":"e_1_3_2_1_12_1","volume-title":"Text tiling: Segmenting text into multi-paragraph subtopic passages[J]. Computational linguistics","author":"Hearst M A","year":"1997","unstructured":"Hearst M A. Text tiling: Segmenting text into multi-paragraph subtopic passages[J]. Computational linguistics, 1997, 23(1): 33-64."},{"key":"e_1_3_2_1_13_1","unstructured":"Riedl M Biemann C. TopicTiling: a text segmentation algorithm based on LDA[C]\/\/Proceedings of ACL 2012 student research workshop. 2012: 37-42."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.5555\/972684.972689"},{"volume-title":"Discourse segmentation of multi-party conversation[C]\/\/Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics. 2003: 562-569","author":"Galley M","key":"e_1_3_2_1_15_1","unstructured":"Galley M, McKeown K, Fosler-Lussier E, Discourse segmentation of multi-party conversation[C]\/\/Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics. 2003: 562-569."},{"key":"e_1_3_2_1_16_1","volume-title":"Unsupervised text segmentation using semantic relatedness graphs[C]\/\/Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics","author":"Glava\u0161 G","year":"2016","unstructured":"Glava\u0161 G, Nanni F, Ponzetto S P. Unsupervised text segmentation using semantic relatedness graphs[C]\/\/Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics. Association for Computational Linguistics, 2016: 125-130."},{"key":"e_1_3_2_1_17_1","volume-title":"Text segmentation as a supervised learning task[J]. arXiv preprint arXiv:1803.09337","author":"Koshorek O","year":"2018","unstructured":"Koshorek O, Cohen A, Mor N, Text segmentation as a supervised learning task[J]. arXiv preprint arXiv:1803.09337, 2018."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"Aumiller D Almasian S Lackner S Structural text segmentation of legal documents[C]\/\/Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law. 2021: 2-11.","DOI":"10.1145\/3462757.3466085"},{"key":"e_1_3_2_1_19_1","volume-title":"Text segmentation by cross segment attention[J]. arXiv preprint arXiv:2004.14535","author":"Lukasik M","year":"2020","unstructured":"Lukasik M, Dadachev B, Simoes G, Text segmentation by cross segment attention[J]. arXiv preprint arXiv:2004.14535, 2020."},{"key":"e_1_3_2_1_20_1","first-page":"334","article-title":"Bayesian unsupervised topic segmentation[C]\/\/Proceedings of the 2008 Conference on Empirical Methods","volume":"2008","author":"Eisenstein J","unstructured":"Eisenstein J, Barzilay R. Bayesian unsupervised topic segmentation[C]\/\/Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing. 2008: 334-343.","journal-title":"Natural Language Processing."},{"key":"e_1_3_2_1_21_1","volume-title":"Dialogue session segmentation by embedding-enhanced texttiling[J]. arXiv preprint arXiv:1610.03955","author":"Song Y","year":"2016","unstructured":"Song Y, Mou L, Yan R, Dialogue session segmentation by embedding-enhanced texttiling[J]. arXiv preprint arXiv:1610.03955, 2016."},{"key":"e_1_3_2_1_22_1","volume-title":"Topic-aware multi-turn dialogue modeling[C]\/\/Proceedings of the AAAI Conference on Artificial Intelligence","author":"Xu Y","year":"2021","unstructured":"Xu Y, Zhao H, Zhang Z. 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Extending the entity grid with entity-specific features[C]\/\/Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. 2011: 125-129.","journal-title":"Human Language Technologies."},{"key":"e_1_3_2_1_26_1","volume-title":"Extending the entity-based coherence model with multiple ranks[C]\/\/Proceedings of the 13th Conference of the European","author":"Feng V W","year":"2012","unstructured":"Feng V W, Hirst G. Extending the entity-based coherence model with multiple ranks[C]\/\/Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics. 2012: 315-324."},{"volume-title":"Graph-based local coherence modeling[C]\/\/Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2013: 93-103","author":"Guinaudeau C","key":"e_1_3_2_1_27_1","unstructured":"Guinaudeau C, Strube M. 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