We present in this paper an application that automatically generates textual short-term weather forecasts for every municipality in Galicia (NW Spain), using the real data provided by the Galician Meteorology Agency (MeteoGalicia). This solution combines in an innovative way computing with perceptions techniques and strategies for linguistic description of data, together with a natural language generation (NLG) system. The application, which is named GALiWeather, extracts relevant information from weather forecast input data and encodes it into intermediate descriptions using linguistic variables and temporal references. These descriptions are later translated into natural language texts by the NLG system. The obtained forecast results have been thoroughly validated by an expert meteorologist from MeteoGalicia using a quality assessment methodology, which covers two key dimensions of a text: the accuracy of its content and the correctness of its form. Following this validation, GALiWeather will be released as a real service, offering custom forecasts for a wide public.

The SoftLearn Activity Reporter is a data‐to‐text service, which automatically generates textual reports about the activity developed by students within the SoftLearn virtual learning environment. In this paper, we describe the conception of the service, its architecture, and its subsequent evaluation by an expert pedagogue, where 20 full reports generated from real data from an undergraduate course supported by the SoftLearn platform were assessed. Results show that the automatically generated reports are a valuable complementary tool for explaining teachers and students the information comprised in a learning analytics dashboard.

This paper presents an empirical research. It focuses on testing empirically the benefits of providing users, in a specific domain, with textual interpretation of the fuzzy inferences carried out by a fuzzy classifier for a given selection of samples. The hypothesis to test is as follows: “Users understand easier the decision made by a fuzzy system when they are provided with a textual interpretation of the fuzzy inference mechanism that the system carried out”. This hypothesis was successfully tested in a web survey. The application domain was leaf classification. The fuzzy classifiers were built with the GUAJE fuzzy modeling open source software which is aimed at generating interpretable fuzzy systems. The textual interpretation was handmade by an expert who followed the guidelines of the Natural Language Generation approach proposed by Reiter and Dale. Reported results encourage us to go on with a series of additional experiments devoted to deeply explore how Natural Language Generation techniques can contribute to facilitate the understanding of fuzzy systems.