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Recent work in the field of automatic summarization and headline generationfocuses on maximizing ROUGE scores for various news datasets. We present analternative, extrinsic, evaluation metric for this task, Answering Performancefor Evaluation of Summaries. APES utilizes recent progress in the field ofreading-comprehension to quantify the ability of a summary to answer a set ofmanually created questions regarding central entities in the source article. Wefirst analyze the strength of this metric by comparing it to known manualevaluation metrics. We then present an end-to-end neural abstractive model thatmaximizes APES, while increasing ROUGE scores to competitive results.

Question Answering as an Automatic Evaluation Metric for News Article Summarization