  State-of-the-art abstractive summarization systems frequently hallucinate
content that is not supported by the source document, mainly due to noise in
the training dataset. Existing methods opt to drop the noisy samples or tokens
from the training set entirely, reducing the effective training set size and
creating an artificial propensity to copy words from the source. In this work,
we propose a training objective for abstractive summarization based on
rejection learning, in which the model learns whether or not to reject
potentially noisy tokens. We further propose a regularized decoding objective
that penalizes non-factual candidate summaries during inference by using the
rejection probability learned during training. We show that our method
considerably improves the factuality of generated summaries in automatic and
human evaluations when compared to five baseline models and that it does so
while increasing the abstractiveness of the generated summaries.
