Since the late 1990s, automatic text simplification (ATS) was promoted as a natural
language processing (NLP) task with great potential to make texts more accessible to …

Background The use of artificial intelligence (AI)–based tools in the care of individual
patients and patient populations is rapidly expanding. Objective The aim of this paper is to …

We report findings of the TSAR-2022 shared task on multilingual lexical simplification,
organized as part of the Workshop on Text Simplification, Accessibility, and Readability …

Text Simplification improves the readability of sentences through several rewriting
transformations, such as lexical paraphrasing, deletion, and splitting. Current simplification …

Although powerful pretrained language models generate high-quality output text, they bring
new concerns about the potential misuse of such models for malicious purposes. Natural …

Text content created by humans or language models is often stolen or misused by
adversaries. Tracing text provenance can help claim the ownership of text content or identify …

This work presents Keep it Simple (KiS), a new approach to unsupervised text simplification
which learns to balance a reward across three properties: fluency, salience and simplicity …

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 …

In the short text, the extremely short length, feature sparsity, and high ambiguity pose huge
challenges to classification tasks. Recently, as an effective method for tuning Pre-trained …

Lexical simplification (LS) aims at replacing complex words with simpler alternatives. LS
commonly consists of three main steps: complex word identification, substitute generation …