Thanks for your comments and opinions.

Since we are dealing with temporal (sequential) data (i.e., text in our case), an architecture using which such data type can be modelled is required. Theoretically and practically, RNN is the best option for such problems. RNN is not just a single algorithm with one single architecture, but an strategy with many variants and architectures, which can be modified and improved based on the problem we wish to solve.

The generated result are heavily dependent on the keywords provided. However, you may still receive some generic results, which is because of the dataset used for training the model. We are working on a new training set with higher quality so that the model can learn more specific features rather than the generic ones.