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        <title>Question Answering as an Automatic Evaluation Metric for News Article Summarization</title>
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        <namePart type="given">Matan</namePart>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Question Answering as an Automatic Evaluation Metric for News Article Summarization
%A Eyal, Matan
%A Baumel, Tal
%A Elhadad, Michael
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F eyal-etal-2019-question
%X 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.
%R 10.18653/v1/N19-1395
%U https://aclanthology.org/N19-1395
%U https://doi.org/10.18653/v1/N19-1395
%P 3938-3948

Markdown (Informal)

[Question Answering as an Automatic Evaluation Metric for News Article Summarization](https://aclanthology.org/N19-1395) (Eyal et al., NAACL 2019)

• Question Answering as an Automatic Evaluation Metric for News Article Summarization (Eyal et al., NAACL 2019)

ACL

• Matan Eyal, Tal Baumel, and Michael Elhadad. 2019. Question Answering as an Automatic Evaluation Metric for News Article Summarization. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 3938–3948, Minneapolis, Minnesota. Association for Computational Linguistics.