Abstractive conversation summarization has received much attention recently.
However, these generated summaries often suffer from insufficient, redundant,
or incorrect content, largely due to the unstructured and complex
characteristics of human-human interactions. To this end, we propose to
explicitly model the rich structures in conversations for more precise and
accurate conversation summarization, by first incorporating discourse relations
between utterances and action triples ("who-doing-what") in utterances through
structured graphs to better encode conversations, and then designing a
multi-granularity decoder to generate summaries by combining all levels of
information. Experiments show that our proposed models outperform
state-of-the-art methods and generalize well in other domains in terms of both
automatic evaluations and human judgments. We have publicly released our code
at this https URL

该研究提出了通过使用结构化图表来建模话语关系和行动三元组，并设计多粒度解码器来生成摘要的方法，从而实现更准确、精确的抽象对话摘要。实验结果表明，该模型在自动评估和人类判断方面优于现有方法，并在其他领域具有很强的泛化能力。