The task of Automatic Text Simplification (ATS) aims to transform texts to
improve their readability and comprehensibility. Current solutions are based
on Large Language Models (LLM). These models have high performance but
require powerful computing resources and large amounts of data to be
fine-tuned when working in specific and technical domains. This prevents
most researchers from adapting the models to their area of study. The main
contributions of this research are as follows: (1) proposing an accurate
solution when powerful resources are not available, using the transfer
learning capabilities across different domains with a set of linguistic
features using a reduced size pre-trained language model (T5-small) and
making it accessible to a broader range of researchers and individuals; (2)
the evaluation of our model on two well-known datasets, Turkcorpus and
ASSET, and the analysis of the influence of control tokens on the SimpleText
corpus, focusing on the domains of Computer Science and Medicine. Finally, a
detailed discussion comparing our approach with state-of-the-art models for
sentence simplification is included.