TY - GEN
T1 - Question answering as an automatic evaluation metric for news article summarization
AU - Eyal, Matan
AU - Baumel, Tal
AU - Elhadad, Michael
N1 - Funding Information:
This research was supported by the Lynn and William Frankel Centre for Computer Science at Ben-Gurion University.
Publisher Copyright:
© 2019 Association for Computational Linguistics
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85084316691&partnerID=8YFLogxK
U2 - 10.18653/v1/N19-1395
DO - 10.18653/v1/N19-1395
M3 - Conference contribution
AN - SCOPUS:85084316691
T3 - NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference
SP - 3938
EP - 3948
BT - Long and Short Papers
PB - Association for Computational Linguistics (ACL)
T2 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2019
Y2 - 2 June 2019 through 7 June 2019
ER -
