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Predictive process monitoring techniques leverage machine learning (ML) to predict future characteristics of a case, such as the process outcome or the remaining run time. Available techniques employ various models and different types of input data to produce accurate predictions. However, from a practical perspective, explainability is another important requirement besides accuracy since predictive process monitoring techniques frequently support decision-making in critical domains. Techniques from the area of explainable artificial intelligence (XAI) aim to provide this capability and create transparency and interpretability for black-box ML models. While several explainable predictive process monitoring techniques exist, none of them leverages textual data. This is surprising since textual data can provide a rich context to a process that numerical features cannot capture. Recognizing this, we use this paper to …