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.

翻译：在客户服务系统中,对话总结可以自动为长话对话制作摘要,使客户和代理人试图讨论特定主题的问题,从而提高服务效率。在这项工作中,我们注重专题对话总结,产生高度抽象的总结,保留对话中的主要想法。在口头对话中,大量对话噪音和通用语义可能会掩盖基本信息内容,使一般主题建模方法难以适用。此外,对于客户服务而言,特定角色的信息事项是摘要不可或缺的组成部分。为了有效地进行对话主题建模并捕捉多功能信息,我们在此工作中提议了一个新颖的专题强化两阶段对话总结器(TDS),与一个突出的注重主题的线性神经学主题模型(SATM)共同促进客户服务对话的专题化。关于现实世界中国客户服务数据集的综合研究显示,我们的方法优于几个强有力的基线。