It was interesting to see a take on the problem from the researchers outside of NLP or ML fields, but the authors only considered classic LDA and PLSA for comparison. I am not currently involved in topic modeling, but I know there exist techniques and modifications to classic models that improve topic discovery (like tf-idf weighting). Can you suggest any modern methods from NLP and ML communities that address the same issues and can rival the authors' findings?