  Dialogue summarization comes with its own peculiar challenges as opposed to
news or scientific articles summarization. In this work, we explore four
different challenges of the task: handling and differentiating parts of the
dialogue belonging to multiple speakers, negation understanding, reasoning
about the situation, and informal language understanding. Using a pretrained
sequence-to-sequence language model, we explore speaker name substitution,
negation scope highlighting, multi-task learning with relevant tasks, and
pretraining on in-domain data. Our experiments show that our proposed
techniques indeed improve summarization performance, outperforming strong
baselines.
