Papers › Keep it Simple: Unsupervised Simplification of Multi-Paragraph Text

Keep it Simple: Unsupervised Simplification of Multi-Paragraph Text

7 Jul 2021ACL 2021 5arXiv:2107.03444archive 2025-07-28

Philippe Laban, Tobias Schnabel, Paul Bennett, Marti A. Hearst

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

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