Abstract: Various embodiments of the present disclosure provide machine learning training techniques for implementing a multi-phase training process to holistically train a machine learning summarization model. The multi-phase training process may include generating, using the machine learning summarization model, a training summary for a training transcript. A first, second, and/or a third reward metric may be generated based on the training summary. Each reward metric may be tailored to a different aspect of the machine learning summarization model. For example, the first reward may be based on a comparison between the training summary and a target summary corresponding to the training transcript. The second reward may be based on the training summary and a positive/negative summary. The third reward may be based on training key phrases from the training summary. The model may be trained by optimizing an aggregated reward metric derived from the first, second, and/or third reward metrics.

Abstract: A method and apparatus for validating models is provided where pre-validation of at least one model specific for at least one risk type is performed followed by validation of the model. The validation includes both quantitative and qualitative validation using various tests, approaches, and other criterion. The results of the validation operations are compiled and reported in a validation report.