Large language model classifiers do not directly offer transparency: it is not clear why one class is chosen over another. In this work, summaries explaining the suicide risk level assigned using a fine-tuned mental-roberta-base model are generated from key phrases extracted using SHAP explainability using Mistral-7B. The training data for the classifier consists of all Reddit posts of a user in the University of Maryland Reddit Suicidality Dataset, Version 2, with their suicide risk labels along with selected features extracted from each post by the Linguistic Inquiry and Word Count (LIWC-22) tool. The resulting model is used to make predictions regarding risk on each post of the users in the evaluation set of the CLPsych 2024 shared task, with a SHAP explainer used to identify the phrases contributing to the top scoring, correct and severe risk categories. Some basic stoplisting is applied to the extracted phrases, along with length based filtering, and a locally run version of Mistral-7B-Instruct-v0.1 is used to create summaries from the highest value (based on SHAP) phrases.