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Access to information is a fundamental human right that contributes to freedom of expression and self-determination. However, information availability alone is not enough: the way in which information is expressed and presented on paper or in digital format can be extremely complicated to understand and act upon for a highly diverse set of people with varying ranges of reading, writing and understanding abilities. For a long time, artificial intelligence (AI) and natural language processing (NLP) have dedicated research efforts in the field of automatic text simplification to the development of methods to automate the production of easy-to-read texts. However, in spite of AI hype, the problem persists. Current NLP models such as large language models (LLMs) are still not well understood, and their use in the development of text simplification needs careful assessment. In this paper, having identified the need for easy-to-read, accessible texts, we provide a light overview of current methods in NLP and provide a lay explanation of several techniques applied to lexically and syntactically modified texts to make them simpler to read and understand. We conclude by raising awareness on the use of general tools to address this very challenging problem that can affect contemporary lives.

En este artículo se presentan los resultados de un estudio empírico llevado a cabo con un corpus paralelo de textos originales y simplificados a mano, y una posterior encuesta online, con el objetivo de identificar operaciones de simplificación de expresiones numéricas en español. Consideramos una "expresión numérica" como una frase que expresa una cantidad que puede venir acompañada de un modificador numérico, como por ejemplo casi un cuarto. Los resultados se analizan considerando las expresiones numéricas en oraciones con y sin contexto, a partir del análisis del corpus y del análisis de los resultados recogidos en la encuesta. Consideramos como trabajo futuro llevar a cabo una implementación computacional de las reglas de transformación extraídas.