Answers Unite! Unsupervised Metrics for Reinforced Summarization Models

Thomas Scialom, Sylvain Lamprier, Benjamin Piwowarski, Jacopo Staiano

Abstract
Abstractive summarization approaches based on Reinforcement Learning (RL) have recently been proposed to overcome classical likelihood maximization. RL enables to consider complex, possibly non differentiable, metrics that globally assess the quality and relevance of the generated outputs. ROUGE, the most used summarization metric, is known to suffer from bias towards lexical similarity as well as from sub-optimal accounting for fluency and readability of the generated abstracts. We thus explore and propose alternative evaluation measures: the reported human-evaluation analysis shows that the proposed metrics, based on Question Answering, favorably compare to ROUGE – with the additional property of not requiring reference summaries. Training a RL-based model on these metrics leads to improvements (both in terms of human or automated metrics) over current approaches that use ROUGE as reward.
Anthology ID:
D19-1320
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:
3246–3256
Language:
URL:
https://aclanthology.org/D19-1320
DOI:
10.18653/v1/D19-1320
Bibkey:
Cite (ACL):
Thomas Scialom, Sylvain Lamprier, Benjamin Piwowarski, and Jacopo Staiano. 2019. Answers Unite! Unsupervised Metrics for Reinforced Summarization Models. 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 3246–3256, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Answers Unite! Unsupervised Metrics for Reinforced Summarization Models (Scialom et al., EMNLP-IJCNLP 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-1/D19-1320.pdf
Code
 recitalAI/summa-qa +  additional community code
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