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.

本文提出了一种基于主题增强的两阶段对话摘要生成器并结合注意力感知神经主题模型 (SATM) 来进行主题定向对话摘要，展示了其在客户服务数据集上的优越性。