  Abstractive summarization systems based on pretrained language models often
generate coherent but factually inconsistent sentences. In this paper, we
present a counterfactual data augmentation approach where we augment data with
perturbed summaries that increase the training data diversity. Specifically, we
present three augmentation approaches based on replacing (i) entities from
other and the same category and (ii) nouns with their corresponding WordNet
hypernyms. We show that augmenting the training data with our approach improves
the factual correctness of summaries without significantly affecting the ROUGE
score. We show that in two commonly used summarization datasets (CNN/Dailymail
and XSum), we improve the factual correctness by about 2.5 points on average
