SUM-QE: a BERT-based Summary Quality Estimation Model

Stratos Xenouleas, Prodromos Malakasiotis, Marianna Apidianaki, Ion Androutsopoulos


Abstract
We propose SUM-QE, a novel Quality Estimation model for summarization based on BERT. The model addresses linguistic quality aspects that are only indirectly captured by content-based approaches to summary evaluation, without involving comparison with human references. SUM-QE achieves very high correlations with human ratings, outperforming simpler models addressing these linguistic aspects. Predictions of the SUM-QE model can be used for system development, and to inform users of the quality of automatically produced summaries and other types of generated text.
Anthology ID:
D19-1618
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
6005–6011
Language:
URL:
https://aclanthology.org/D19-1618
DOI:
10.18653/v1/D19-1618
Bibkey:
Cite (ACL):
Stratos Xenouleas, Prodromos Malakasiotis, Marianna Apidianaki, and Ion Androutsopoulos. 2019. SUM-QE: a BERT-based Summary Quality Estimation Model. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 6005–6011, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
SUM-QE: a BERT-based Summary Quality Estimation Model (Xenouleas et al., EMNLP-IJCNLP 2019)
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https://aclanthology.org/D19-1618.pdf
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