  Proposal of large-scale datasets has facilitated research on deep neural
models for news summarization. Deep learning can also be potentially useful for
spoken dialogue summarization, which can benefit a range of real-life scenarios
including customer service management and medication tracking. To this end, we
propose DialogSum, a large-scale labeled dialogue summarization dataset. We
conduct empirical analysis on DialogSum using state-of-the-art neural
summarizers. Experimental results show unique challenges in dialogue
summarization, such as spoken terms, special discourse structures, coreferences
and ellipsis, pragmatics and social common sense, which require specific
representation learning technologies to better deal with.
