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Text summarization is one of the most challenging and interesting problems inNLP. Although much attention has been paid to summarizing structured text likenews reports or encyclopedia articles, summarizing conversations---an essentialpart of human-human/machine interaction where most important pieces ofinformation are scattered across various utterances of differentspeakers---remains relatively under-investigated. This work proposes amulti-view sequence-to-sequence model by first extracting conversationalstructures of unstructured daily chats from different views to representconversations and then utilizing a multi-view decoder to incorporate differentviews to generate dialogue summaries. Experiments on a large-scale dialoguesummarization corpus demonstrated that our methods significantly outperformedprevious state-of-the-art models via both automatic evaluations and humanjudgment. We also discussed specific challenges that current approaches facedwith this task. We have publicly released our code athttps://github.com/GT-SALT/Multi-View-Seq2Seq.

Multi-View Sequence-to-Sequence Models with Conversational Structure for Abstractive Dialogue Summarization