Text simplification (TS) reduces the complexity of the text to improve its readability and
understandability, while possibly retaining its original information content. Over time, TS has …

Even in highly-developed countries, as many as 15–30% of the population can only
understand texts written using a basic vocabulary. Their understanding of everyday texts is …

Lexical Simplification is the process of replacing complex words in a given sentence with
simpler alternatives of equivalent meaning. This task has wide applicability both as an …

Unsupervised pretraining models have been shown to facilitate a wide range of downstream
NLP applications. These models, however, retain some of the limitations of traditional static …

Lexical Simplification (LS) is the task of substituting complex words within a sentence for
simpler alternatives while maintaining the sentence's original meaning. LS is the lexical …

Randomly masking text spans in ordinary texts in the pre-training stage hardly allows
models to acquire the ability to generate simple texts. It can hurt the performance of pre …

We present a new Lexical Simplification approach that exploits Neural Networks to learn
substitutions from the Newsela corpus-a large set of professionally produced simplifications …

Availability of corpora with technical and simplified contents is crucial for the development
and test of methods for text simplification. We describe this kind of corpus for the French …

The purpose of automatic text simplification is to adapt the content of documents in order to
make them easier to understand by a given population or to improve the performance of …

Text Simplification (TS) aims to reduce the linguistic complexity of content to make it easier
to understand. Research in TS has been of keen interest, especially as approaches to TS …