Question: How are the different components of the model trained? Is it trained end-to-end?
Answer: the objective of our model is sum of the two processes, jointly trained using "teacher-forcing" algorithm, we feed the ground-truth summary to each decoder and minimize the objective, At test time, each time step we choose the predicted word by $\hat{y} = argmax_{y^{\prime }} P(y^{\prime }|x)$ , use beam search to generate the draft summaries, and use greedy search to generate the refined summaries., the model can be trained end-to-end
Alternative answer: the model can be trained end-to-end