An Interpretable Fuzzy Framework for Data-to-Text Generation Using Linguistic Contexts and Computational Perceptions: A Case Study on Photovoltaic Stations

Textual and visual representations of data play a key role in data science and artificial intelligence by supporting effective and user-friendly communication. Among existing approaches, automatic data-to-text generation aims to produce natural language descriptions from structured data sources. This paper presents an interpretable fuzzy framework for generating data to text based on linguistic contexts and computational perception networks evaluated through formal concept analysis. The proposed framework is organized into four main stages: (i) transforming numerical data sets into linguistic contexts, (ii) generating computational perceptions from linguistic contexts, (iii) building computational perceptions networks to automatically generate natural language summaries, and (iv) validating the generated texts through comparison with summaries obtained using formal concept analysis–based baselines. To the best of our knowledge, this is the first work to address the generation of linguistic summaries through an interpretable process that transforms data into linguistic contexts and subsequently into computational perceptions. Another key difference from previous work lies in the verification of the linguistic summaries generated through these computational perceptions by using a formal method. A software prototype was implemented and evaluated using real photovoltaic station data provided by a local energy operator in Puerto Real (Cádiz, Spain). Experimental results show that the proposed fuzzy framework improves the interpretability and consistency of the generated summaries when compared with others approaches, demonstrating its potential for explainable and user-centered data-to-text generation.