We have just submitted a paper to Information Sciences introducing PLENARY that is a framework for explaining black-box models in natural language through fuzzy linguistic summaries. PLENARY is an explainable classifier based on a data- driven predictive model. Neural learning is exploited to derive a predictive model considering two levels of labels associated with data. Then, model explanations are derived through the pop- ular SHapley Additive exPlanations (SHAP) tool and conveyed in a linguistic form via fuzzy linguistic summaries. The linguistic summarization enable translating explanations of the model outputs provided by SHAP into statements expressed in natural language. PLENARY accounts for the imprecision related to model outputs by summarizing them into simple linguistic state- ments, and for the imprecision related to the data labeling process by inclusion of additional do- main knowledge in form of middle-layer labels. PLENARY is validated on preprocessed speech signals collected from smartphones of bipolar disorder patients and on publicly available data about mental health surveys. The experiments confirm that fuzzy linguistic summarization is an efficient technique to support meta-analyses of outputs from AI models. Also, PLENARY improves the explainability due to the aggregation of low-level attributes into higher informa- tion granules, and due to the comprehensive incorporation of vague domain knowledge into a multi-task sequential and compositional multi-layer perceptron. Fingers crossed for the review process!