  We study generating abstractive summaries that are faithful and factually
consistent with the given articles. A novel contrastive learning formulation is
presented, which leverages both reference summaries, as positive training data,
and automatically generated erroneous summaries, as negative training data, to
train summarization systems that are better at distinguishing between them. We
further design four types of strategies for creating negative samples, to
resemble errors made commonly by two state-of-the-art models, BART and PEGASUS,
found in our new human annotations of summary errors. Experiments on XSum and
CNN/Daily Mail show that our contrastive learning framework is robust across
datasets and models. It consistently produces more factual summaries than
strong comparisons with post error correction, entailment-based reranking, and
unlikelihood training, according to QA-based factuality evaluation. Human
judges echo the observation and find that our model summaries correct more
errors.
