@inproceedings{xenouleas-etal-2019-sum,
title = "{SUM}-{QE}: a {BERT}-based Summary Quality Estimation Model",
author = "Xenouleas, Stratos and
Malakasiotis, Prodromos and
Apidianaki, Marianna and
Androutsopoulos, Ion",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "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 = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1618",
doi = "10.18653/v1/D19-1618",
pages = "6005--6011",
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.",
}
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%0 Conference Proceedings
%T SUM-QE: a BERT-based Summary Quality Estimation Model
%A Xenouleas, Stratos
%A Malakasiotis, Prodromos
%A Apidianaki, Marianna
%A Androutsopoulos, Ion
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F xenouleas-etal-2019-sum
%X 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.
%R 10.18653/v1/D19-1618
%U https://aclanthology.org/D19-1618
%U https://doi.org/10.18653/v1/D19-1618
%P 6005-6011
Markdown (Informal)
[SUM-QE: a BERT-based Summary Quality Estimation Model](https://aclanthology.org/D19-1618) (Xenouleas et al., EMNLP-IJCNLP 2019)
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.