Dialogue summarization involves a wide range of scenarios and domains. However, existing methods generally only apply to specific scenarios or domains. In this study, we propose a new pre-trained model specifically designed for multi-scenario multi-domain dialogue summarization. It adopts a multi-stage pre-training strategy to reduce the gap between the pre-training objective and fine-tuning objective. Specifically, we first conduct domain-aware pre-training using large-scale multi-scenario multi-domain dialogue data to enhance the adaptability of our pre-trained model. Then, we conduct task-oriented pre-training using large-scale multi-scenario multi-domain "dialogue-summary" parallel data annotated by ChatGPT to enhance the dialogue summarization ability of our pre-trained model. Experimental results on three dialogue summarization datasets from different scenarios and domains indicate that our pre-trained model significantly outperforms previous state-of-the-art models in full fine-tuning, zero-shot, and few-shot settings.

翻译：对话摘要涉及广泛的场景和领域，但现有方法通常仅适用于特定场景或领域。本研究提出一种专门针对多场景多领域对话摘要的预训练模型，采用多阶段预训练策略以缩小预训练目标与微调目标之间的差距。具体而言，我们首先使用大规模多场景多领域对话数据进行领域感知预训练，以增强预训练模型的自适应性；随后，利用ChatGPT标注的大规模多场景多领域“对话-摘要”平行数据进行任务导向预训练，以提升模型的对话摘要能力。在三个不同场景和领域的对话摘要数据集上的实验结果表明，本预训练模型在全量微调、零样本和少样本设置下均显著优于此前的最优模型。