A collection of text simplification datasets with a focus on sentence/paragraph/document-level simplification. All contributions are welcome!
For browsing/sorting the datasets, you can use the interactive table.
Notes on the table columns:
- Kind refers to the way simplification instances were obtained. For parallel, this is usually through manual simplification according to specific guidelines. For comparable, this is by automatically mining pairs of complex/simple sentences with similar meaning from a large text corpus.
- Level can be lexical (lex), sentence (sent), paragraph (para) or document (doc).
- Refs refers to the number of references per instance (i.e., gold simplifications).
{{datasets}}
New entries can be added to data.yml. Afterwards, run python render.py and submit a PR with the changes.
This list has greatly benefitted from the survey of Alva-Manchego et al. (2020) and Štajner (2021), as well as notes by Laura Vásquez-Rodríguez. Thanks! Also thanks to @tollefj for adding the interactive table.