It is one of the largest freely available resources for text simplification, and unlike the previously used EW-SEW corpus 2 (Kauchak, 2013), which only contains full matches (167K pairs), the newer dataset also contains partial matches.
it is not only larger, but it also allows for learning sentence shortening (dropping irrelevant parts) transformations (see Table 3  We use the Stanford NER system (Finkel et al., 2005) to get an approximate number of locations, persons, organizations and miscellaneous entities in the corpus.