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Text simplification research has mostly focused on sentence-levelsimplification, even though many desirable edits - such as adding relevantbackground information or reordering content - may require document-levelcontext. Prior work has also predominantly framed simplification as asingle-step, input-to-output task, only implicitly modeling the fine-grained,span-level edits that elucidate the simplification process. To address bothgaps, we introduce the SWiPE dataset, which reconstructs the document-levelediting process from English Wikipedia (EW) articles to paired Simple Wikipedia(SEW) articles. In contrast to prior work, SWiPE leverages the entire revisionhistory when pairing pages in order to better identify simplification edits. Wework with Wikipedia editors to annotate 5,000 EW-SEW document pairs, labelingmore than 40,000 edits with proposed 19 categories. To scale our efforts, wepropose several models to automatically label edits, achieving an F-1 score ofup to 70.6, indicating that this is a tractable but challenging NLU task.Finally, we categorize the edits produced by several simplification models andfind that SWiPE-trained models generate more complex edits while reducingunwanted edits.

SWiPE: A Dataset for Document-Level Simplification of Wikipedia Pages