Automatic text simplification (TS) aims to automate the process of rewriting text to make it
easier for people to read. A pre-requisite for TS to be useful is that it should convey …

Transformer-based models have achieved state-of-the-art performance on natural language
understanding (NLU) tasks by learning important token relationships through the attention …

Knowledge Graph (KG)-to-Text Generation has seen recent improvements in generating
fluent and informative sentences which describe a given KG. As KGs are widespread across …

" Leichte Sprache", the German counterpart to Simple English, is a regulated language
aiming to facilitate complex written language that would otherwise stay inaccessible to …

Text simplification aims to make the text easier to understand by applying rewriting
transformations. There has been very little research on Chinese text simplification for a long …

In recent years, pretrained neural language models (PNLMs) have taken the field of natural
language processing by storm, achieving new benchmarks and state-of-the-art …

Text simplification is one of the domains in Natural Language Processing (NLP) that offers
an opportunity to understand the text in a simplified manner for exploration. However, it is …

This study explores the overlap between text summarization and simplification outputs.
While summarization evaluation methods are streamlined, simplification lacks cohesion …

Chinese is a complex language, creating a demand for text simplification in various contexts.
Traditional text simplification models have primarily relied on pre-trained models. With the …

Text simplification seeks to improve readability while retaining the original content and
meaning. Our study investigates whether pre-trained classifiers also maintain such …