Imbalanced multi-label classification using multi-task learning with extractive summarization

16 Mar 2019John Brandt

Extractive summarization and imbalanced multi-label classification often require vast amounts of training data to avoid overfitting. In situations where training data is expensive to generate, leveraging information between tasks is an attractive approach to increasing the amount of available information... (read more)

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