Keep it Simple: Unsupervised Simplification of Multi-Paragraph Text
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. Code available: https://github.com/tingofurro/keep_it_simple
Cite
@article{arxiv.2107.03444,
title = {Keep it Simple: Unsupervised Simplification of Multi-Paragraph Text},
author = {Philippe Laban and Tobias Schnabel and Paul Bennett and Marti A. Hearst},
journal= {arXiv preprint arXiv:2107.03444},
year = {2021}
}
Comments
Accepted at ACL-IJCNLP 2021, 14 pages, 7 figures