Topic-Oriented Spoken Dialogue Summarization for Customer Service with Saliency-Aware Topic Modeling

Authors

  • Yicheng Zou Fudan University
  • Lujun Zhao Alibaba Group
  • Yangyang Kang Alibaba Group
  • Jun Lin Alibaba Group
  • Minlong Peng Fudan University
  • Zhuoren Jiang Zhejiang University
  • Changlong Sun Alibaba Group Zhejiang University
  • Qi Zhang Fudan University
  • Xuanjing Huang Fudan University
  • Xiaozhong Liu Indiana University Bloomington

DOI:

https://doi.org/10.1609/aaai.v35i16.17723

Keywords:

Applications, Summarization

Abstract

In a customer service system, dialogue summarization can boost service efficiency by automatically creating summaries for long spoken dialogues in which customers and agents try to address issues about specific topics. In this work, we focus on topic-oriented dialogue summarization, which generates highly abstractive summaries that preserve the main ideas from dialogues. In spoken dialogues, abundant dialogue noise and common semantics could obscure the underlying informative content, making the general topic modeling approaches difficult to apply. In addition, for customer service, role-specific information matters and is an indispensable part of a summary. To effectively perform topic modeling on dialogues and capture multi-role information, in this work we propose a novel topic-augmented two-stage dialogue summarizer (TDS) jointly with a saliency-aware neural topic model (SATM) for topic-oriented summarization of customer service dialogues. Comprehensive studies on a real-world Chinese customer service dataset demonstrated the superiority of our method against several strong baselines.

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Published

2021-05-18

How to Cite

Zou, Y., Zhao, L., Kang, Y., Lin, J., Peng, M., Jiang, Z., Sun, C., Zhang, Q., Huang, X., & Liu, X. (2021). Topic-Oriented Spoken Dialogue Summarization for Customer Service with Saliency-Aware Topic Modeling. Proceedings of the AAAI Conference on Artificial Intelligence, 35(16), 14665-14673. https://doi.org/10.1609/aaai.v35i16.17723

Issue

Section

AAAI Technical Track on Speech and Natural Language Processing III