  Despite significant progress has been achieved in text summarization, factual
inconsistency in generated summaries still severely limits its practical
applications. Among the key factors to ensure factual consistency, a reliable
automatic evaluation metric is the first and the most crucial one. However,
existing metrics either neglect the intrinsic cause of the factual
inconsistency or rely on auxiliary tasks, leading to an unsatisfied correlation
with human judgments or increasing the inconvenience of usage in practice. In
light of these challenges, we propose a novel metric to evaluate the factual
consistency in text summarization via counterfactual estimation, which
formulates the causal relationship among the source document, the generated
summary, and the language prior. We remove the effect of language prior, which
can cause factual inconsistency, from the total causal effect on the generated
summary, and provides a simple yet effective way to evaluate consistency
without relying on other auxiliary tasks. We conduct a series of experiments on
three public abstractive text summarization datasets, and demonstrate the
advantages of the proposed metric in both improving the correlation with human
judgments and the convenience of usage. The source code is available at
https://github.com/xieyxclack/factual_coco.
