  Recent pre-trained abstractive summarization systems have started to achieve
credible performance, but a major barrier to their use in practice is their
propensity to output summaries that are not faithful to the input and that
contain factual errors. While a number of annotated datasets and statistical
models for assessing factuality have been explored, there is no clear picture
of what errors are most important to target or where current techniques are
succeeding and failing. We explore both synthetic and human-labeled data
sources for training models to identify factual errors in summarization, and
study factuality at the word-, dependency-, and sentence-level. Our
observations are threefold. First, exhibited factual errors differ
significantly across datasets, and commonly-used training sets of simple
synthetic errors do not reflect errors made on abstractive datasets like XSum.
Second, human-labeled data with fine-grained annotations provides a more
effective training signal than sentence-level annotations or synthetic data.
Finally, we show that our best factuality detection model enables training of
more factual XSum summarization models by allowing us to identify non-factual
tokens in the training data.
