Sentence Simplification (SS) aims to modify a sentence in order to make it easier to read and understand. In order to do so, several rewriting transformations can be performed such as replacement, reordering, and splitting. Executing these transformations while keeping sentences grammatical, preserving their main idea, and generating simpler output, is a challenging and still far from solved problem. In this article, we survey research on SS, focusing on approaches that attempt to learn how to simplify using corpora of aligned original-simplified sentence pairs in English, which is the dominant paradigm nowadays. We also include a benchmark of different approaches on common datasets so as to compare them and highlight their strengths and limitations. We expect that this survey will serve as a starting point for researchers interested in the task and help spark new ideas for future developments.

中文翻译：

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数据驱动的句子简化：调查和基准

句子简化 (SS) 旨在修改句子以使其更易于阅读和理解。为此，可以执行多种重写转换，例如替换、重新排序和拆分。在保持句子语法、保留其主要思想并生成更简单的输出的同时执行这些转换是一个具有挑战性且远未解决的问题。在本文中，我们调查了对 SS 的研究，重点是试图学习如何使用英语中对齐的原始简化句对的语料库进行简化的方法，这是当今的主导范式。我们还包括对常见数据集的不同方法的基准，以便比较它们并突出它们的优点和局限性。