Unlike well-structured text, such as news reports and encyclopedia articles,
dialogue content often comes from two or more interlocutors, exchanging
information with each other. In such a scenario, the topic of a conversation
can vary upon progression and the key information for a certain topic is often
scattered across multiple utterances of different speakers, which poses
challenges to abstractly summarize dialogues. To capture the various topic
information of a conversation and outline salient facts for the captured
topics, this work proposes two topic-aware contrastive learning objectives,
namely coherence detection and sub-summary generation objectives, which are
expected to implicitly model the topic change and handle information scattering
challenges for the dialogue summarization task. The proposed contrastive
objectives are framed as auxiliary tasks for the primary dialogue summarization
task, united via an alternative parameter updating strategy. Extensive
experiments on benchmark datasets demonstrate that the proposed simple method
significantly outperforms strong baselines and achieves new state-of-the-art
performance. The code and trained models are publicly available via
\href{https://github.com/Junpliu/ConDigSum}{this https URL}.

通过引入一种叫做对比学习的技术来解决对话总结中信息分散和主题变化的挑战，该方法使用了两个具体的辅助任务 (coherence detection 和 sub-summary generation)，在基于原始对话总结任务上取得了最优性能，显著超过强基准模型。