Text simplification research has mostly focused on sentence-level simplification, even though many desirable edits - such as adding relevant background information or reordering content - may require document-level context. Prior work has also predominantly framed simplification as a single-step, input-to-output task, only implicitly modeling the fine-grained, span-level edits that elucidate the simplification process. To address both gaps, we introduce the SWiPE dataset, which reconstructs the document-level editing process from English Wikipedia (EW) articles to paired Simple Wikipedia (SEW) articles. In contrast to prior work, SWiPE leverages the entire revision history when pairing pages in order to better identify simplification edits. We work with Wikipedia editors to annotate 5,000 EW-SEW document pairs, labeling more than 40,000 edits with proposed 19 categories. To scale our efforts, we propose several models to automatically label edits, achieving an F-1 score of up to 70.6, indicating that this is a tractable but challenging NLU task. Finally, we categorize the edits produced by several simplification models and find that SWiPE-trained models generate more complex edits while reducing unwanted edits.

翻译：文本简化研究大多聚焦于句子级简化，然而许多理想的编辑操作（如添加相关背景信息或重新组织内容）可能需依赖文档级上下文。先前的研究亦主要将简化框架定义为单步输入至输出任务，仅隐式建模可阐明简化过程的细粒度跨度级编辑。为填补这两项空白，我们提出SWiPE数据集，该数据集重构了从英文维基百科（EW）文章到配对简易维基百科（SEW）文章的文档级编辑过程。与先前研究不同，SWiPE在匹配页面时利用完整修订历史以更好识别简化编辑。我们与维基百科编辑合作，对5,000对EW-SEW文档对进行标注，使用提出的19个类别标注超过40,000次编辑。为扩展工作规模，我们提出若干自动标注编辑的模型，F-1分数最高达70.6，表明这是可处理但具有挑战性的自然语言理解任务。最后，我们对多种简化模型生成的编辑进行分类，发现经SWiPE训练的模型能生成更复杂的编辑，同时减少不期望的编辑。