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CASIA OpenIR
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CSDS: A Fine-Grained Chinese Dataset for Customer Service Dialogue Summarization 会议论文
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Online and Punta Cana, Dominican Republic, 2021-11-07 - 2021-11-11
作者:  Lin, Haitao;  Ma, Liqun;  Zhu, Junnan;  Xiang, Lu;  Zhou, Yu;  Zhang, Jiajun;  Zong, Chengqing
Adobe PDF(491Kb)  |  收藏  |  浏览/下载：112/28  |  提交时间：2023/06/13
Other Roles Matter! Enhancing Role-Oriented Dialogue Summarization via Role Interactions 会议论文
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland, 2022-05-22 - 2022-05-27
作者:  Lin, Haitao;  Zhu, Junnan;  Xiang, Lu;  Zhou, Yu;  Zhang, Jiajun;  Zong, Chengqing
Adobe PDF(889Kb)  |  收藏  |  浏览/下载：141/66  |  提交时间：2023/06/13
Dual-View Conditional Variational Auto-Encoder for Emotional Dialogue Generation 期刊论文
ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING, 2022, 卷号: 21, 期号: 3, 页码: 18
作者:  Li, Mei;  Zhang, Jiajun;  Lu, Xiang;  Zong, Chengqing
Adobe PDF(1187Kb)  |  收藏  |  浏览/下载：284/59  |  提交时间：2022/06/10
Sentiment  dialogue  neural networks  
Graph-based Multimodal Ranking Models for Multimodal Summarization 期刊论文
ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING, 2021, 卷号: 20, 期号: 4, 页码: 21
作者:  Zhu, Junnan;  Xiang, Lu;  Zhou, Yu;  Zhang, Jiajun;  Zong, Chengqing
Adobe PDF(4193Kb)  |  收藏  |  浏览/下载：277/51  |  提交时间：2021/12/28
Multimodal summarization  single-modal  multimodal ranking  unsupervised  
Medical Term and Status Generation From Chinese Clinical Dialogue With Multi-Granularity Transformer 期刊论文
IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, 2021, 卷号: 29, 页码: 3362-3374
作者:  Li, Mei;  Xiang, Lu;  Kang, Xiaomian;  Zhao, Yang;  Zhou, Yu;  Zong, Chengqing
Adobe PDF(3036Kb)  |  收藏  |  浏览/下载：266/57  |  提交时间：2021/12/28
Medical diagnostic imaging  Transformers  Task analysis  Medical services  Computational modeling  Semantics  Data mining  Medical dialogue  multi-granularity  attention mechanism  natural language understanding  sequence to sequence learning