Dialogue summarization has recently garnered significant attention due to its
wide range of applications. However, existing methods for summarizing dialogues
are suboptimal because they do not take into account the inherent structure of
dialogue and rely heavily on labeled data, which can lead to poor performance
in new domains. In this work, we propose DIONYSUS (dynamic input optimization
in pre-training for dialogue summarization), a pre-trained encoder-decoder
model for summarizing dialogues in any new domain. To pre-train DIONYSUS, we
create two pseudo summaries for each dialogue example: one is produced by a
fine-tuned summarization model, and the other is a collection of dialogue turns
that convey important information. We then choose one of these pseudo summaries
based on the difference in information distribution across different types of
dialogues. This selected pseudo summary serves as the objective for
pre-training DIONYSUS using a self-supervised approach on a large dialogue
corpus. Our experiments show that DIONYSUS outperforms existing methods on six
datasets, as demonstrated by its ROUGE scores in zero-shot and few-shot
settings.

本研究提出了一种名为 DIONYSUS 的预训练编码器 - 解码器模型，通过自监督学习方法在大型对话语料库上进行预训练，能够在任何新的领域中概括对话。实验表明，与现有方法相比，DIONYSUS 在零样本和少样本情境下的 ROUGE 评分上表现更好。