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With the rapid increase in the volume of dialogue data from daily life, thereis a growing demand for dialogue summarization. Unfortunately, training a largesummarization model is generally infeasible due to the inadequacy of dialoguedata with annotated summaries. Most existing works for low-resource dialoguesummarization directly pretrain models in other domains, e.g., the news domain,but they generally neglect the huge difference between dialogues andconventional articles. To bridge the gap between out-of-domain pretraining andin-domain fine-tuning, in this work, we propose a multi-source pretrainingparadigm to better leverage the external summary data. Specifically, we exploitlarge-scale in-domain non-summary data to separately pretrain the dialogueencoder and the summary decoder. The combined encoder-decoder model is thenpretrained on the out-of-domain summary data using adversarial critics, aimingto facilitate domain-agnostic summarization. The experimental results on twopublic datasets show that with only limited training data, our approachachieves competitive performance and generalizes well in different dialoguescenarios.

Low-Resource Dialogue Summarization with Domain-Agnostic Multi-Source Pretraining