  Randomly masking text spans in ordinary texts in the pre-training stage
hardly allows models to acquire the ability to generate simple texts. It can
hurt the performance of pre-trained models on text simplification tasks. In
this paper, we propose a new continued pre-training strategy to teach the
pre-trained model to generate simple texts. We continue pre-training BART, a
representative model, to obtain SimpleBART. It consistently and significantly
improves the results on lexical simplification, sentence simplification, and
document-level simplification tasks over BART. At the end, we compare
SimpleBART with several representative large language models (LLMs).
