@inproceedings{laban2021keep,
author = {Laban, Philippe and Schnabel, Tobias and Bennett, Paul and Hearst, Marti A.},
title = {Keep It Simple: Unsupervised Simplification of Multi-Paragraph Text},
booktitle = {ACL-IJCNLP 2021},
year = {2021},
month = {July},
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 on GitHub.},
url = {http://approjects.co.za/?big=en-us/research/publication/keep-it-simple-unsupervised-simplification-of-multi-paragraph-text/},
}