Incorporating Knowledge into Document Summarization: an Application of Prefix-Tuning on GPT-2

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.