Despite the great development of document summarization techniques nowadays, factual inconsistencies between the generated summaries and the original text still occur from time to time. This paper proposes a prefix-tuning-based approach that uses a set of trainable continuous prefix prompt together with discrete prompts to aid model generation, which makes a significant impact on both CNN/Daily Mail and XSum summaries generated using GPT-2. The improvements on fact preservation in the generated summaries indicates the effectiveness of adopting this prefix-tuning-based method in knowledge-enhanced document summarization, and also shows a great potential on other natural language processing tasks.

翻译：尽管当今文档摘要技术取得了巨大发展，但生成的摘要与原文之间仍时常出现事实不一致的问题。本文提出一种基于前缀调优的方法，该方法通过一组可训练的连续前缀提示与离散提示相结合来辅助模型生成，在利用GPT-2生成的CNN/Daily Mail和XSum摘要上均取得了显著效果。生成摘要中事实保留能力的提升表明，这种基于前缀调优的方法在知识增强型文档摘要中具有有效性，同时也显示出其在其他自然语言处理任务中的巨大潜力。