  Abstractive dialogue summarization is a challenging task for several reasons.
First, most of the important pieces of information in a conversation are
scattered across utterances through multi-party interactions with different
textual styles. Second, dialogues are often informal structures, wherein
different individuals express personal perspectives, unlike text summarization,
tasks that usually target formal documents such as news articles. To address
these issues, we focused on the association between utterances from individual
speakers and unique syntactic structures. Speakers have unique textual styles
that can contain linguistic information, such as voiceprint. Therefore, we
constructed a syntax-aware model by leveraging linguistic information (i.e.,
POS tagging), which alleviates the above issues by inherently distinguishing
sentences uttered from individual speakers. We employed multi-task learning of
both syntax-aware information and dialogue summarization. To the best of our
knowledge, our approach is the first method to apply multi-task learning to the
dialogue summarization task. Experiments on a SAMSum corpus (a large-scale
dialogue summarization corpus) demonstrated that our method improved upon the
vanilla model. We further analyze the costs and benefits of our approach
relative to baseline models.
