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

本论文旨在通过引入对话行为，设计一种基于句子门控机制的神经摘要模型，以更好地模拟对话行为和摘要之间的关系，从而提高其在 AMI 会议语料库上的性能表现。实验结果表明，所提出的模型相对于现有的基线表现有很大改善，突显出对话行为所提供的交互信号的实用性。