Text simplification aims to make the text easier to understand by applying rewriting transformations. There has been very little research on Chinese text simplification for a long time. The lack of generic evaluation data is an essential reason for this phenomenon. In this paper, we introduce MCTS, a multi-reference Chinese text simplification dataset. We describe the annotation process of the dataset and provide a detailed analysis. Furthermore, we evaluate the performance of several unsupervised methods and advanced large language models. We additionally provide Chinese text simplification parallel data that can be used for training, acquired by utilizing machine translation and English text simplification. We hope to build a basic understanding of Chinese text simplification through the foundational work and provide references for future research. All of the code and data are released at https://github.com/blcuicall/mcts/.

翻译：文本简化旨在通过重写变换使文本更易于理解。长期以来，针对中文文本简化的研究非常有限。缺乏通用的评价数据是造成这种现象的重要原因。本文介绍了MCTS，一个多参考中文文本简化数据集。我们描述了该数据集的标注过程并进行了详细分析。此外，我们评估了多种无监督方法和先进大型语言模型的性能。我们还额外提供了可用于训练的中文文本简化平行语料，该语料通过利用机器翻译和英文文本简化获得。我们期望通过这项基础工作建立对中文文本简化的基本认识，并为未来研究提供参考。所有代码和数据均可在https://github.com/blcuicall/mcts/获取。