  The most advanced abstractive dialogue summarizers lack generalization
ability on new domains and the existing researches for domain adaptation in
summarization generally rely on large-scale pre-trainings. To explore the
lightweight fine-tuning methods for domain adaptation of dialogue
summarization, in this paper, we propose an efficient and generalizable
Domain-Oriented Prefix-tuning model, which utilizes a domain word initialized
prefix module to alleviate domain entanglement and adopts discrete prompts to
guide the model to focus on key contents of dialogues and enhance model
generalization. We conduct zero-shot experiments and build domain adaptation
benchmarks on two multi-domain dialogue summarization datasets, TODSum and
QMSum. Adequate experiments and qualitative analysis prove the effectiveness of
our methods.
