7shows, the decoder's learning objective is to minimize negative likelihood of conditional probability, in which y * t is the t-th ground truth word of summary.
a decoder with this structure is not sufficient enough: if we use the BERT network in this decoder, then during training and inference, in-complete context(part of sentence) is fed into the BERT module, and although we can finetune BERT's parameters, the input distribution is quite different from the pre-train process, and thus harms the quality of generated context representations.