  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.
