  Recent work in the field of automatic summarization and headline generation
focuses on maximizing ROUGE scores for various news datasets. We present an
alternative, extrinsic, evaluation metric for this task, Answering Performance
for Evaluation of Summaries. APES utilizes recent progress in the field of
reading-comprehension to quantify the ability of a summary to answer a set of
manually created questions regarding central entities in the source article. We
first analyze the strength of this metric by comparing it to known manual
evaluation metrics. We then present an end-to-end neural abstractive model that
maximizes APES, while increasing ROUGE scores to competitive results.
