Interactive Area Topics Extraction With Policy Gradient

Extracting representative topics and improving the extraction performance is rather challenging. In this work, we formulate a novel problem, called Interactive Area Topics Extraction, and propose a learning interactive topics extraction (LITE) model to regard this problem as a sequential decision making process and construct an end-to-end framework to use interaction with users. In particular, we use recurrent neural network (RNN) decoder to address the problem and policy gradient method to tune the model parameters considering user feedback. Experimental result has shown the effectiveness of the proposed framework.