  With the abundance of automatic meeting transcripts, meeting summarization is
of great interest to both participants and other parties. Traditional methods
of summarizing meetings depend on complex multi-step pipelines that make joint
optimization intractable. Meanwhile, there are a handful of deep neural models
for text summarization and dialogue systems. However, the semantic structure
and styles of meeting transcripts are quite different from articles and
conversations. In this paper, we propose a novel abstractive summary network
that adapts to the meeting scenario. We design a hierarchical structure to
accommodate long meeting transcripts and a role vector to depict the difference
among speakers. Furthermore, due to the inadequacy of meeting summary data, we
pretrain the model on large-scale news summary data. Empirical results show
that our model outperforms previous approaches in both automatic metrics and
human evaluation. For example, on ICSI dataset, the ROUGE-1 score increases
from 34.66% to 46.28%.
