Correcting Diverse Factual Errors in Abstractive Summarization via Post-Editing and Language Model Infilling

Abstractive summarization models often generate inconsistent summaries containing factual errors or hallucinated content. Recent works focus on correcting factual errors in generated summaries via post-editing. Such correction models are trained using adversarial nonfactual summaries constructed using heuristic rules for injecting errors. However, generating non-factual summaries using heuristics often does not generalize well to actual model errors. In this work, we propose to generate hard, representative synthetic examples of nonfactual summaries through infilling language models. With this data, we train a more robust fact-correction model to post-edit the summaries to improve factual consistency. Through quantitative and qualitative experiments on two popular summarization datasets-CNN/DM and XSum-we show that our approach vastly outperforms prior methods in correcting erroneous summaries. Our model-FACTEDITimproves factuality scores by over ∼11 points on CNN/DM and over ∼31 points on XSum on average across multiple summarization models, producing more factual summaries while maintaining competitive summarization quality. 1 The first vaccine for Ebola was approved by the FDA in 2019 in the US, five years after the initial outbreak in 2014. To produce the vaccine, scientists had to sequence the DNA of Ebola, then identify possible vaccines, and finally show successful clinical trials. Scientists say a vaccine for COVID-19 is unlikely to be ready this year, although clinical trials have already started. Scientists believe a vaccine for Covid-19 might not be ready this year. The first vaccine for Ebola took 5 years to be approved by the FDA. Scientists believe a vaccine for Ebola might not be ready this year. The first vaccine for Ebola took 5 years to be produced by the CBP.