  Text simplification, crucial in natural language processing, aims to make
texts more comprehensible, particularly for specific groups like visually
impaired Spanish speakers, a less-represented language in this field. In
Spanish, there are few datasets that can be used to create text simplification
systems. Our research has the primary objective to develop a Spanish financial
text simplification dataset. We created a dataset with 5,314 complex and
simplified sentence pairs using established simplification rules. We also
compared our dataset with the simplifications generated from GPT-3, Tuner, and
MT5, in order to evaluate the feasibility of data augmentation using these
systems. In this manuscript we present the characteristics of our dataset and
the findings of the comparisons with other systems. The dataset is available at
Hugging face, saul1917/FEINA.
