STAR: Syntax- and Topic-Aware Role Dialogue Summarization
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5669-8_1
Reference18 articles.
1. Lin, H., et al.: CSDS: a finegrained Chinese dataset for customer service dialogue summarization. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing,EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7–11 November 2021, pp. 4436–4451 (2021)
2. Lin, H., Zhu, J., Xiang, L., Zhou, Y., Zhang, J., Zong, C.: Other roles matter! enhancing role-oriented dialogue summarization via role interactions. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, vol. 1: Long Papers, pp. 2545–2558 (2022)
3. Liang, X., Wu, S., Cui, C., Bai, J., Bian, C., Li, Z.: Enhancing dialogue summarization with topic-aware global- and local- level centrality. In: Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pp. 27–38 (2023)
4. Zheng, H., Lapata, M.: Sentence centrality revisited for unsupervised summarization. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 6236–6247 (2019)
5. Feng, X., Feng, X., Qin, L., Qin, B., Liu, T.: Language model as an annotator: Exploring DialoGPT for dialogue summarization. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, vol. 1: Long Papers, pp. 1479–1491 (2021)
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