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Neural abstractive summarization has been increasingly studied, where theprior work mainly focused on summarizing single-speaker documents (news,scientific publications, etc). In dialogues, there are different interactionsbetween speakers, which are usually defined as dialogue acts. The interactivesignals may provide informative cues for better summarizing dialogues. Thispaper proposes to explicitly leverage dialogue acts in a neural summarizationmodel, where a sentence-gated mechanism is designed for modeling therelationship between dialogue acts and the summary. The experiments show thatour proposed model significantly improves the abstractive summarizationperformance compared to the state-of-the-art baselines on AMI meeting corpus,demonstrating the usefulness of the interactive signal provided by dialogueacts.

Abstractive Dialogue Summarization with Sentence-Gated Modeling Optimized by Dialogue Acts