  We propose a simple method for post-processing the outputs of a text
summarization system in order to refine its overall quality. Our approach is to
train text-to-text rewriting models to correct information redundancy errors
that may arise during summarization. We train on synthetically generated noisy
summaries, testing three different types of noise that introduce out-of-context
information within each summary. When applied on top of extractive and
abstractive summarization baselines, our summary denoising models yield metric
improvements while reducing redundancy.
