PromptSum: Parameter-Efficient Controllable Abstractive Summarization
This work addresses the problem of efficient and controllable summarization for NLP practitioners, offering an incremental improvement by adapting prompt tuning to generation tasks.
The paper tackles the challenge of achieving parameter-efficient, data-efficient, and controllable abstractive summarization by introducing PromptSum, which combines prompt tuning with a multi-task objective and discrete entity prompts, resulting in competitive ROUGE scores on benchmarks while tuning significantly fewer parameters.
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.