  Modern summarization models generate highly fluent but often factually
unreliable outputs. This motivated a surge of metrics attempting to measure the
factuality of automatically generated summaries. Due to the lack of common
benchmarks, these metrics cannot be compared. Moreover, all these methods treat
factuality as a binary concept and fail to provide deeper insights into the
kinds of inconsistencies made by different systems. To address these
limitations, we devise a typology of factual errors and use it to collect human
annotations of generated summaries from state-of-the-art summarization systems
for the CNN/DM and XSum datasets. Through these annotations, we identify the
proportion of different categories of factual errors in various summarization
models and benchmark factuality metrics, showing their correlation with human
judgment as well as their specific strengths and weaknesses.
