@inproceedings{laban-etal-2021-keep,
title = "Keep It Simple: Unsupervised Simplification of Multi-Paragraph Text",
author = "Laban, Philippe and
Schnabel, Tobias and
Bennett, Paul and
Hearst, Marti A.",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.498",
doi = "10.18653/v1/2021.acl-long.498",
pages = "6365--6378",
abstract = "This work presents Keep it Simple (KiS), a new approach to unsupervised text simplification which learns to balance a reward across three properties: fluency, salience and simplicity. We train the model with a novel algorithm to optimize the reward (k-SCST), in which the model proposes several candidate simplifications, computes each candidate{'}s reward, and encourages candidates that outperform the mean reward. Finally, we propose a realistic text comprehension task as an evaluation method for text simplification. When tested on the English news domain, the KiS model outperforms strong supervised baselines by more than 4 SARI points, and can help people complete a comprehension task an average of 18{\%} faster while retaining accuracy, when compared to the original text.",
}
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<abstract>This work presents Keep it Simple (KiS), a new approach to unsupervised text simplification which learns to balance a reward across three properties: fluency, salience and simplicity. We train the model with a novel algorithm to optimize the reward (k-SCST), in which the model proposes several candidate simplifications, computes each candidate’s reward, and encourages candidates that outperform the mean reward. Finally, we propose a realistic text comprehension task as an evaluation method for text simplification. When tested on the English news domain, the KiS model outperforms strong supervised baselines by more than 4 SARI points, and can help people complete a comprehension task an average of 18% faster while retaining accuracy, when compared to the original text.</abstract>
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%0 Conference Proceedings
%T Keep It Simple: Unsupervised Simplification of Multi-Paragraph Text
%A Laban, Philippe
%A Schnabel, Tobias
%A Bennett, Paul
%A Hearst, Marti A.
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F laban-etal-2021-keep
%X This work presents Keep it Simple (KiS), a new approach to unsupervised text simplification which learns to balance a reward across three properties: fluency, salience and simplicity. We train the model with a novel algorithm to optimize the reward (k-SCST), in which the model proposes several candidate simplifications, computes each candidate’s reward, and encourages candidates that outperform the mean reward. Finally, we propose a realistic text comprehension task as an evaluation method for text simplification. When tested on the English news domain, the KiS model outperforms strong supervised baselines by more than 4 SARI points, and can help people complete a comprehension task an average of 18% faster while retaining accuracy, when compared to the original text.
%R 10.18653/v1/2021.acl-long.498
%U https://aclanthology.org/2021.acl-long.498
%U https://doi.org/10.18653/v1/2021.acl-long.498
%P 6365-6378
Markdown (Informal)
[Keep It Simple: Unsupervised Simplification of Multi-Paragraph Text](https://aclanthology.org/2021.acl-long.498) (Laban et al., ACL-IJCNLP 2021)
ACL
- Philippe Laban, Tobias Schnabel, Paul Bennett, and Marti A. Hearst. 2021. Keep It Simple: Unsupervised Simplification of Multi-Paragraph Text. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 6365–6378, Online. Association for Computational Linguistics.