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This work presents Keep it Simple (KiS), a new approach to unsupervised textsimplification which learns to balance a reward across three properties:fluency, salience and simplicity. We train the model with a novel algorithm tooptimize the reward (k-SCST), in which the model proposes several candidatesimplifications, computes each candidate's reward, and encourages candidatesthat outperform the mean reward. Finally, we propose a realistic textcomprehension task as an evaluation method for text simplification. When testedon the English news domain, the KiS model outperforms strong supervisedbaselines by more than 4 SARI points, and can help people complete acomprehension task an average of 18% faster while retaining accuracy, whencompared to the original text. Code available:https://github.com/tingofurro/keep_it_simple

Keep it Simple: Unsupervised Simplification of Multi-Paragraph Text