Considering that the F1 gain value might be negative in the labeled data, we follow previous works (Ren et al., 2017) to use Min-Max Normalization to rescale the gain value to [0, 1]: We then apply a softmax operation with temperature τ (Hinton et al., 2015) 1 to produce the labeled data distribution Q as the training target.
we apply the temperature τ as a smoothing factor to produce a smoothed label distribution Q: we minimize the KL loss function J: 5 Experiments A large scale dataset is essential for training neural network-based summarization models.