Modeling Encoder-Decoder according to instructions from a paper
What is the input and output? Assuming your input is a sequence of documents, you would need to convert it into some sort of embeddings (TF-IDF, any other). After that comes the RNN - which looks ok to me. The output of the RNN is equal to the number of timesteps (num of words in each doc?). For each timestep we predict using a Dense final layer. You have 9 outputs. Is it some sort of a classification across 9 different types of categories or levels of citation counts?
Overall looks ok with the above assumptions other than the fact that you are feeding raw data instead of using an embedding layer