Keep It Simple: Unsupervised Simplification of Multi-Paragraph Text
Philippe Laban, Tobias Schnabel, Paul Bennett, Marti A. Hearst
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.
BibTeX
@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"
}