SWiPE: A Dataset for Document-Level Simplification of Wikipedia Pages
This addresses the problem of limited document-level simplification resources for NLP researchers, though it is incremental as it builds on existing Wikipedia-based simplification work.
The authors tackled the lack of document-level text simplification datasets by introducing SWiPE, a dataset with 5,000 annotated Wikipedia document pairs and over 40,000 edits, achieving up to 70.6 F-1 score for automatic edit labeling and showing that SWiPE-trained models produce more complex edits with fewer unwanted ones.
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.