  Chinese sentence simplification faces challenges due to the lack of
large-scale labeled parallel corpora and the prevalence of idioms. To address
these challenges, we propose Readability-guided Idiom-aware Sentence
Simplification (RISS), a novel framework that combines data augmentation
techniques with lexcial simplification. RISS introduces two key components: (1)
Readability-guided Paraphrase Selection (RPS), a method for mining high-quality
sentence pairs, and (2) Idiom-aware Simplification (IAS), a model that enhances
the comprehension and simplification of idiomatic expressions. By integrating
RPS and IAS using multi-stage and multi-task learning strategies, RISS
outperforms previous state-of-the-art methods on two Chinese sentence
simplification datasets. Furthermore, RISS achieves additional improvements
when fine-tuned on a small labeled dataset. Our approach demonstrates the
potential for more effective and accessible Chinese text simplification.
