  In real-world scenarios, labeled samples for dialogue summarization are
usually limited (i.e., few-shot) due to high annotation costs for high-quality
dialogue summaries. To efficiently learn from few-shot samples, previous works
have utilized massive annotated data from other downstream tasks and then
performed prompt transfer in prompt tuning so as to enable cross-task knowledge
transfer. However, existing general-purpose prompt transfer techniques lack
consideration for dialogue-specific information. In this paper, we focus on
improving the prompt transfer from dialogue state tracking to dialogue
summarization and propose Skeleton-Assisted Prompt Transfer (SAPT), which
leverages skeleton generation as extra supervision that functions as a medium
connecting the distinct source and target task and resulting in the model's
better consumption of dialogue state information. To automatically extract
dialogue skeletons as supervised training data for skeleton generation, we
design a novel approach with perturbation-based probes requiring neither
annotation effort nor domain knowledge. Training the model on such skeletons
can also help preserve model capability during prompt transfer. Our method
significantly outperforms existing baselines. In-depth analyses demonstrate the
effectiveness of our method in facilitating cross-task knowledge transfer in
few-shot dialogue summarization.
