  Neural abstractive summarization systems have achieved promising progress,
thanks to the availability of large-scale datasets and models pre-trained with
self-supervised methods. However, ensuring the factual consistency of the
generated summaries for abstractive summarization systems is a challenge. We
propose a post-editing corrector module to address this issue by identifying
and correcting factual errors in generated summaries. The neural corrector
model is pre-trained on artificial examples that are created by applying a
series of heuristic transformations on reference summaries. These
transformations are inspired by an error analysis of state-of-the-art
summarization model outputs. Experimental results show that our model is able
to correct factual errors in summaries generated by other neural summarization
models and outperforms previous models on factual consistency evaluation on the
CNN/DailyMail dataset. We also find that transferring from artificial error
correction to downstream settings is still very challenging.
