Abstract: Systems, devices, and techniques are disclosed for lossless summarization. Data that including indications of whether a text summary generated by a machine learning system from an input document includes correct answers to questions about the input document may be received. Weights associated with the questions about the input document may be received. A first vector may be generated from the indications in the data. A second vector may be generated from the weights. A dot product of the first vector and the second vector may be determined. A ratio of a word count of the text summary to the word count of the input document may be determined. A score for the text summary based on the dot product and the ratio may be determined. The machine learning system may be adjusted based on the score.

Abstract: Methods, systems, and devices for distributed hyperparameter tuning and load balancing are described. A device (e.g., an application server) may generate a first set of combinations of hyperparameter values associated with training a mathematical model. The mathematical model may include a machine learning model, an optimization model, or any combination. The device may identify a subset of combinations from the first set of combinations that are associated with a computational runtime that exceeds a first threshold and may distribute the subset of combinations across a set of machines. The device may then test each of the first set of combinations in a parallel processing operation to generate a first set of validation error values and may test a second set of combinations of hyperparameter values using an objective function that is based on the first set of validation error values.

Abstract: Systems, devices, and techniques are disclosed for lossless summarization. Data that including indications of whether a text summary generated by a machine learning system from an input document includes correct answers to questions about the input document may be received. Weights associated with the questions about the input document may be received. A first vector may be generated from the indications in the data. A second vector may be generated from the weights. A dot product of the first vector and the second vector may be determined. A ratio of a word count of the text summary to the word count of the input document may be determined. A score for the text summary based on the dot product and the ratio may be determined. The machine learning system may be adjusted based on the score.