  With the rapid increase in the volume of dialogue data from daily life, there
is a growing demand for dialogue summarization. Unfortunately, training a large
summarization model is generally infeasible due to the inadequacy of dialogue
data with annotated summaries. Most existing works for low-resource dialogue
summarization directly pretrain models in other domains, e.g., the news domain,
but they generally neglect the huge difference between dialogues and
conventional articles. To bridge the gap between out-of-domain pretraining and
in-domain fine-tuning, in this work, we propose a multi-source pretraining
paradigm to better leverage the external summary data. Specifically, we exploit
large-scale in-domain non-summary data to separately pretrain the dialogue
encoder and the summary decoder. The combined encoder-decoder model is then
pretrained on the out-of-domain summary data using adversarial critics, aiming
to facilitate domain-agnostic summarization. The experimental results on two
public datasets show that with only limited training data, our approach
achieves competitive performance and generalizes well in different dialogue
scenarios.
