Prompt tuning (PT), a parameter-efficient technique that only tunes the additional prompt embeddings while keeping the backbone pre-trained language model (PLM) frozen, has shown promising results in language understanding tasks, especially in low-resource scenarios. However, effective prompt design methods suitable for generation tasks such as summarization are still lacking. At the same time, summarization guided through instructions (discrete prompts) can achieve a desirable double objective of high quality and controllability in summary generation. Towards a goal of strong summarization performance under the triple conditions of parameter-efficiency, data-efficiency, and controllability, we introduce PromptSum, a method combining PT with a multi-task objective and discrete entity prompts for abstractive summarization. Our model achieves competitive ROUGE results on popular abstractive summarization benchmarks coupled with a strong level of controllability through entities, all while only tuning several orders of magnitude less parameters.

翻译：提示调优（Prompt Tuning，PT）是一种仅调整额外提示嵌入而冻结骨干预训练语言模型（PLM）的参数高效技术，已在语言理解任务中展现出良好效果，尤其是在低资源场景下。然而，目前仍缺乏适用于摘要等生成任务的有效提示设计方法。同时，通过指令（离散提示）引导的摘要生成能够在摘要生成中实现高质量与可控性的双重目标。为在参数高效、数据高效和可控性三重条件下实现强大的摘要性能，我们提出了PromptSum，一种将提示调优与多任务目标及离散实体提示相结合的方法，用于抽象式摘要生成。我们的模型在流行的抽象式摘要基准测试中取得了具有竞争力的ROUGE分数，同时通过实体实现了高度的可控性，且仅需调优数个数量级更少的参数。