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The issue of factual consistency in abstractive summarization has receivedextensive attention in recent years, and the evaluation of factual consistencybetween summary and document has become an important and urgent task. Most ofthe current evaluation metrics are adopted from the question answering (QA) ornatural language inference (NLI) task. However, the application of QA-basedmetrics is extremely time-consuming in practice while NLI-based metrics arelack of interpretability. In this paper, we propose a cloze-based evaluationframework called ClozE and show the great potential of the cloze-based metric.It inherits strong interpretability from QA, while maintaining the speed ofNLI- level reasoning. We demonstrate that ClozE can reduce the evaluation timeby nearly 96% relative to QA-based metrics while retaining theirinterpretability and performance through experiments on six human-annotateddatasets and a meta-evaluation benchmark GO FIGURE (Gabriel et al., 2021).Finally, we discuss three important facets of ClozE in practice, which furthershows better overall performance of ClozE compared to other metrics.

Just ClozE! A Novel Framework for Evaluating the Factual Consistency Faster in Abstractive Summarization