{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T07:01:35Z","timestamp":1761894095337,"version":"build-2065373602"},"publisher-location":"Singapore","reference-count":46,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819527243","type":"print"},{"value":"9789819527250","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-2725-0_20","type":"book-chapter","created":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T05:18:48Z","timestamp":1761887928000},"page":"322-338","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["TAG: Dialogue Summarization Based on\u00a0Topic Segmentation and\u00a0Graph Structures"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7052-9538","authenticated-orcid":false,"given":"Yatian","family":"Shen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qichao","family":"Hao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guosong","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Songyang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eryan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,1]]},"reference":[{"key":"20_CR1","doi-asserted-by":"publisher","unstructured":"Adilazuarda, M.F., Cahyawijaya, S., Winata, G.I., Purwarianti, A., Aji, A.F.: LinguAlchemy: fusing typological and geographical elements for unseen language generalization. In: Al-Onaizan, Y., Bansal, M., Chen, Y.-N. (eds.) Findings of the Association for Computational Linguistics: EMNLP 2024, pp. 3912\u20133928. Association for Computational Linguistics, Miami, USA (2024). https:\/\/doi.org\/10.18653\/v1\/2024.findings-emnlp.225","DOI":"10.18653\/v1\/2024.findings-emnlp.225"},{"key":"20_CR2","unstructured":"Purwarianti, A., et al.: NusaDialogue: dialogue summarization and generation for underrepresented and extremely low-resource languages. In: Proceedings of the Second Workshop on South East Asian Language Processing, pp. 82\u2013100 (2025)"},{"key":"20_CR3","doi-asserted-by":"crossref","unstructured":"Zhong, M., et al.: QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization. arXiv:2104.05938 (2021). https:\/\/arxiv.org\/abs\/2104.05938","DOI":"10.18653\/v1\/2021.naacl-main.472"},{"key":"20_CR4","unstructured":"Tang, C., Zhang, H., Loakman, T., Lin, C., Guerin, F.: Enhancing Dialogue Generation via Dynamic Graph Knowledge Aggregation. arXiv:2306.16195 (2023). https:\/\/arxiv.org\/abs\/2306.16195"},{"key":"20_CR5","doi-asserted-by":"crossref","unstructured":"Zhao, L., Xu, W., Guo, J.: Improving abstractive dialogue summarization with graph structures and topic words. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 437\u2013449 (2020)","DOI":"10.18653\/v1\/2020.coling-main.39"},{"key":"20_CR6","doi-asserted-by":"crossref","unstructured":"Hua, Y., Deng, Z., McKeown, K.: Improving long dialogue summarization with semantic graph representation. In: Findings of the Association for Computational Linguistics: ACL 2023, pp. 13851\u201313883 (2023)","DOI":"10.18653\/v1\/2023.findings-acl.871"},{"key":"20_CR7","unstructured":"Chen, J., Dodda, M., Yang, D.: Human-in-the-Loop Abstractive Dialogue Summarization. arXiv:2212.09750 (2022). https:\/\/arxiv.org\/abs\/2212.09750"},{"key":"20_CR8","unstructured":"Huang, K.H., et al.: SWING: Balancing Coverage and Faithfulness for Dialogue Summarization. arXiv:2301.10483 (2023). https:\/\/arxiv.org\/abs\/2301.10483"},{"key":"20_CR9","doi-asserted-by":"crossref","unstructured":"Park, S., Lee, J.: Unsupervised abstractive dialogue summarization with word graphs and POV conversion. arXiv:2205.13108 (2022). https:\/\/arxiv.org\/abs\/2205.13108","DOI":"10.18653\/v1\/2022.wit-1.1"},{"key":"20_CR10","unstructured":"Rennard, V., Shang, G., Vazirgiannis, M., Hunter, J.: Leveraging discourse structure for extractive meeting summarization. arXiv:2405.11055 (2024). https:\/\/arxiv.org\/abs\/2405.11055"},{"key":"20_CR11","unstructured":"Feng, X., Feng, X., Qin, B., Geng, X., Liu, T.: Dialogue discourse-aware graph convolutional networks for abstractive meeting summarization. arXiv:2012.03502 (2020). https:\/\/arxiv.org\/abs\/2012.03502"},{"key":"20_CR12","doi-asserted-by":"crossref","unstructured":"Chen, J., Yang, D.: Multi-view sequence-to-sequence models with conversational structure for abstractive dialogue summarization. arXiv:2010.01672 (2020). https:\/\/arxiv.org\/abs\/2010.01672","DOI":"10.18653\/v1\/2020.emnlp-main.336"},{"issue":"7","key":"20_CR13","doi-asserted-by":"publisher","first-page":"3965","DOI":"10.1007\/s00500-022-07534-6","volume":"27","author":"RC Belwal","year":"2023","unstructured":"Belwal, R.C., Rai, S., Gupta, A.: Extractive text summarization using clustering-based topic modeling. Soft. Comput. 27(7), 3965\u20133982 (2023)","journal-title":"Soft. Comput."},{"key":"20_CR14","unstructured":"Wang, H., Li, P., Fan, Y., Zhu, Q.: Simulating dual-process thinking in dialogue topic shift detection. In: Proceedings of the 31st International Conference on Computational Linguistics, pp. 2592\u20132602 (2025)"},{"key":"20_CR15","doi-asserted-by":"crossref","unstructured":"Rahman, N., Borah, B.: Redundancy removal method for multi-document query-based text summarization. In: Proceedings of the 2021 International Symposium on Electrical, Electronics and Information Engineering, pp. 568\u2013574 (2021)","DOI":"10.1145\/3459104.3459197"},{"key":"20_CR16","doi-asserted-by":"crossref","unstructured":"Liang, X., Wu, S., Cui, C., Bai, J., Bian, C., Li, Z.: Enhancing dialogue summarization with topic-aware global-and local-level centrality. arXiv:2301.12376 (2023). https:\/\/arxiv.org\/abs\/2301.12376","DOI":"10.18653\/v1\/2023.eacl-main.2"},{"key":"20_CR17","doi-asserted-by":"crossref","unstructured":"Feng, X., Feng, X., Qin, L., Qin, B., Liu, T.: Language model as an annotator: exploring DialoGPT for dialogue summarization. arXiv:2105.12544 (2021). https:\/\/arxiv.org\/abs\/2105.12544","DOI":"10.18653\/v1\/2021.acl-long.117"},{"key":"20_CR18","doi-asserted-by":"crossref","unstructured":"Mitra, A., Paul, S.: Analyzing social networks with dynamic graphs: unravelling the ever-evolving connections. In: Applied Graph Data Science, pp. 195\u2013214. Elsevier (2025)","DOI":"10.1016\/B978-0-443-29654-3.00020-X"},{"key":"20_CR19","doi-asserted-by":"crossref","unstructured":"Ezquerro, A., Vilares, D., G\u00f3mez-Rodr\u00edguez, C.: Dependency graph parsing as sequence labeling. arXiv:2410.17972 (2024). https:\/\/arxiv.org\/abs\/2410.17972","DOI":"10.18653\/v1\/2024.emnlp-main.659"},{"key":"20_CR20","unstructured":"Lei, Y., Huang, R.: Sentence-level media bias analysis with event relation graph. arXiv:2404.01722 (2024). https:\/\/arxiv.org\/abs\/2404.01722"},{"key":"20_CR21","doi-asserted-by":"crossref","unstructured":"Yang, Y., Huang, H., Gao, Y., Li, J.: Building knowledge-grounded dialogue systems with graph-based semantic modelling. Knowl.-Based Syst. 298, 111943 (2024)","DOI":"10.1016\/j.knosys.2024.111943"},{"key":"20_CR22","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1613\/jair.1523","volume":"22","author":"G Erkan","year":"2004","unstructured":"Erkan, G., Radev, D.R.: Lexrank: graph-based lexical centrality as salience in text summarization. J. Artif. Intell. Res. 22, 457\u2013479 (2004)","journal-title":"J. Artif. Intell. Res."},{"key":"20_CR23","unstructured":"Mihalcea, R., Tarau, P.: TextRank: bringing order into text. In: Proceedings of EMNLP 2004, pp. 404\u2013411 (2004)"},{"key":"20_CR24","doi-asserted-by":"crossref","unstructured":"Yasunaga, M., Zhang, R., Meelu, K., Pareek, A., Srinivasan, K., Radev, D.: Graph-based neural multi-document summarization. arXiv:1706.06681 (2017). https:\/\/arxiv.org\/abs\/1706.06681","DOI":"10.18653\/v1\/K17-1045"},{"key":"20_CR25","doi-asserted-by":"crossref","unstructured":"Xu, J., Gan, Z., Cheng, Y., Liu, J.: Discourse-aware neural extractive text summarization. arXiv:1910.14142 (2019). https:\/\/arxiv.org\/abs\/1910.14142","DOI":"10.18653\/v1\/2020.acl-main.451"},{"key":"20_CR26","doi-asserted-by":"crossref","unstructured":"Lewis, M., et al.: BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv:1910.13461 (2019). https:\/\/arxiv.org\/abs\/1910.13461","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"20_CR27","doi-asserted-by":"crossref","unstructured":"Freeman, L.C.: Centrality in social networks: conceptual clarification. In: Social Network: Critical Concepts in Sociology, vol. 1, no. 3, pp. 238\u2013263. Routledge, London (2002)","DOI":"10.1016\/0378-8733(78)90021-7"},{"key":"20_CR28","doi-asserted-by":"crossref","unstructured":"Zhang, Y., et al.: DialoGPT: large-scale generative pre-training for conversational response generation. arXiv:1911.00536 (2019). https:\/\/arxiv.org\/abs\/1911.00536","DOI":"10.18653\/v1\/2020.acl-demos.30"},{"key":"20_CR29","doi-asserted-by":"crossref","unstructured":"Gao, S., et al.: Dialogue summarization with static-dynamic structure fusion graph. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 13858\u201313873. ACL, Toronto, Canada (2023)","DOI":"10.18653\/v1\/2023.acl-long.775"},{"key":"20_CR30","unstructured":"Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 27 (2014)"},{"key":"20_CR31","doi-asserted-by":"crossref","unstructured":"Chen, Y., Liu, Y., Chen, L., Zhang, Y.: DialogSum: a real-life scenario dialogue summarization dataset. arXiv:2105.06762 (2021). https:\/\/arxiv.org\/abs\/2105.06762","DOI":"10.18653\/v1\/2021.findings-acl.449"},{"key":"20_CR32","doi-asserted-by":"crossref","unstructured":"Gliwa, B., Mochol, I., Biesek, M., Wawer, A.: SAMSum corpus: a human-annotated dialogue dataset for abstractive summarization. arXiv:1911.12237 (2019). https:\/\/arxiv.org\/abs\/1911.12237","DOI":"10.18653\/v1\/D19-5409"},{"key":"20_CR33","unstructured":"Duan, J., Lu, F.: DialogES: a large dataset for generating dialogue events and summaries. https:\/\/github.com\/Lafitte1573\/NLCorpora\/tree\/main\/DialogES. Accessed 24 June 2025"},{"key":"20_CR34","doi-asserted-by":"crossref","unstructured":"Lin, H., et al.: CSDS: a fine-grained Chinese dataset for customer service dialogue summarization. arXiv:2108.13139 (2021). https:\/\/arxiv.org\/abs\/2108.13139","DOI":"10.18653\/v1\/2021.emnlp-main.365"},{"key":"20_CR35","unstructured":"Bao, H., et al.: UniLMv2: pseudo-masked language models for unified language model pre-training. In: Proceedings of ICML, pp. 642\u2013652 (2020)"},{"key":"20_CR36","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"20_CR37","doi-asserted-by":"crossref","unstructured":"See, A., Liu, P.J., Manning, C.D.: Get to the point: summarization with pointer-generator networks. arXiv:1704.04368 (2017). https:\/\/arxiv.org\/abs\/1704.04368","DOI":"10.18653\/v1\/P17-1099"},{"key":"20_CR38","doi-asserted-by":"crossref","unstructured":"Srivastava, S., Sharma, G.: OmniVec2: a novel transformer-based network for large-scale multimodal and multitask learning. In: Proceedings of CVPR, pp. 27412\u201327424 (2024)","DOI":"10.1109\/CVPR52733.2024.02588"},{"key":"20_CR39","doi-asserted-by":"crossref","unstructured":"He, H., et al.: CriSPO: multi-aspect critique-suggestion-guided automatic prompt optimization for text generation. In: Proceedings of AAAI Conference on Artificial Intelligence, vol. 39, no. 22, pp. 24014\u201324022 (2025)","DOI":"10.1609\/aaai.v39i22.34575"},{"key":"20_CR40","unstructured":"Kim, S., Joo, S.J., Chae, H., Kim, C., Hwang, S.-W., Yeo, J.: Mind the gap! Injecting commonsense knowledge for abstractive dialogue summarization. arXiv:2209.00930 (2022). https:\/\/arxiv.org\/abs\/2209.00930"},{"key":"20_CR41","doi-asserted-by":"crossref","unstructured":"Qin, C., Zhang, A., Zhang, Z., Chen, J., Yasunaga, M., Yang, D.: Is ChatGPT a general-purpose natural language processing task solver? arXiv:2302.06476 (2023). https:\/\/arxiv.org\/abs\/2302.06476","DOI":"10.18653\/v1\/2023.emnlp-main.85"},{"key":"20_CR42","unstructured":"Lin, C.-Y.: ROUGE: a package for automatic evaluation of summaries. In: Proceedings of Text Summarization Branches Out, pp. 74\u201381 (2004)"},{"key":"20_CR43","unstructured":"Zhang, T., Kishore, V., Wu, F., Weinberger, K., Artzi, Y.: BERTScore: evaluating text generation with BERT. arXiv:1904.09675 (2019). https:\/\/arxiv.org\/abs\/1904.09675"},{"key":"20_CR44","unstructured":"Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11) (2008)"},{"key":"20_CR45","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv:1412.6980 (2014). https:\/\/arxiv.org\/abs\/1412.6980"},{"key":"20_CR46","unstructured":"Shao, Y., et al.: CPT: a pre-trained unbalanced transformer for both Chinese language understanding and generation. arXiv:2109.05729 (2021). https:\/\/arxiv.org\/abs\/2109.05729"}],"container-title":["Lecture Notes in Computer Science","Chinese Computational Linguistics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-2725-0_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T05:19:03Z","timestamp":1761887943000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-2725-0_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,1]]},"ISBN":["9789819527243","9789819527250"],"references-count":46,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-2725-0_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,1]]},"assertion":[{"value":"1 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CCL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China National Conference on Chinese Computational Linguistics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jinan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 August 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 August 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cncl2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/link.springer.com\/conference\/cncl","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}