  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.
