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A desirable property of a reference-based evaluation metric that measures thecontent quality of a summary is that it should estimate how much informationthat summary has in common with a reference. Traditional text overlap basedmetrics such as ROUGE fail to achieve this because they are limited to matchingtokens, either lexically or via embeddings. In this work, we propose a metricto evaluate the content quality of a summary using question-answering (QA).QA-based methods directly measure a summary's information overlap with areference, making them fundamentally different than text overlap metrics. Wedemonstrate the experimental benefits of QA-based metrics through an analysisof our proposed metric, QAEval. QAEval out-performs current state-of-the-artmetrics on most evaluations using benchmark datasets, while being competitiveon others due to limitations of state-of-the-art models. Through a carefulanalysis of each component of QAEval, we identify its performance bottlenecksand estimate that its potential upper-bound performance surpasses all otherautomatic metrics, approaching that of the gold-standard Pyramid Method.

Towards Question-Answering as an Automatic Metric for Evaluating the Content Quality of a Summary