Following the formula, we can calculate the contribution of every input word makes to every output word, forming a contribution matrix of size $M \times N$, where $N$ is the output sentence length. Given the contribution matrix, we can obtain the word importance of each input word to the entire output sentence. To this end, for each input word, we first aggregate its contribution values to all output words by the sum operation, and then normalize all sums through the Softmax function. 
 Question: How do their models decide how much improtance to give to the output words?
Given the contribution matrix, we can obtain the word importance of each input word to the entire output sentence. 