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Question answering-based summarization evaluation metrics must automaticallydetermine whether the QA model's prediction is correct or not, a task known asanswer verification. In this work, we benchmark the lexical answer verificationmethods which have been used by current QA-based metrics as well as two moresophisticated text comparison methods, BERTScore and LERC. We find that LERCout-performs the other methods in some settings while remaining statisticallyindistinguishable from lexical overlap in others. However, our experimentsreveal that improved verification performance does not necessarily translate tooverall QA-based metric quality: In some scenarios, using a worse verificationmethod -- or using none at all -- has comparable performance to using the bestverification method, a result that we attribute to properties of the datasets.

Benchmarking Answer Verification Methods for Question Answering-Based Summarization Evaluation Metrics