  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 https://github.com/GT-SALT/Structure-Aware-BART.
