2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

Technical Program

Paper Detail

Paper IDHLT-18.1
Paper Title HIERARCHICAL SPEAKER-AWARE SEQUENCE-TO-SEQUENCE MODEL FOR DIALOGUE SUMMARIZATION
Authors Yuejie Lei, Yuanmeng Yan, Zhiyuan Zeng, Keqing He, Ximing Zhang, Weiran Xu, Beijing University of Posts and Telecommunications, China
SessionHLT-18: Language Understanding 6: Summarization and Comprehension
LocationGather.Town
Session Time:Friday, 11 June, 13:00 - 13:45
Presentation Time:Friday, 11 June, 13:00 - 13:45
Presentation Poster
Topic Human Language Technology: [HLT-SDTM] Spoken Document Retrieval and Text Mining
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Virtual Presentation  Click here to watch in the Virtual Conference
Abstract Traditional document summarization models cannot handle dialogue summarization tasks perfectly. In situations with multiple speakers and complex personal pronouns referential relationships in the conversation. The predicted summaries of these models are always full of personal pronoun confusion. In this paper, we propose a hierarchical transformer-based model for dialogue summarization. It encodes dialogues from words to utterances and distinguishes the relationships between speakers and their corresponding personal pronouns clearly. In such a from-coarse-to-fine procedure, our model can generate summaries more accurately and relieve the confusion of personal pronouns. Experiments are based on a dialogue summarization dataset SAMsum, and the results show that the proposed model achieved a comparable result against other strong baselines. Empirical experiments have shown that our method can relieve the confusion of personal pronouns in predicted summaries.