In this paper, we propose to leverage the unique characteristics of dialogues
sharing commonsense knowledge across participants, to resolve the difficulties
in summarizing them. We present SICK, a framework that uses commonsense
inferences as additional context. Compared to previous work that solely relies
on the input dialogue, SICK uses an external knowledge model to generate a rich
set of commonsense inferences and selects the most probable one with a
similarity-based selection method. Built upon SICK, SICK++ utilizes commonsense
as supervision, where the task of generating commonsense inferences is added
upon summarizing the dialogue in a multi-task learning setting. Experimental
results show that with injected commonsense knowledge, our framework generates
more informative and consistent summaries than existing methods.

本论文提出了一种利用对话共享常识知识的唯一特征来解决总结难题的方法，介绍了 SICK 框架，该框架使用常识推理作为额外上下文，比先前仅依赖于输入对话的工作更具优势，实验证明注入常识知识后，我们的框架生成的总结比现有方法更具信息性和一致性。