In this paper, we demonstrate that it is feasible to discover the coherent structure of a text using distributed sentence representations learned in a deep learning framework.
such deep connectionist architectures learn a dense, low-dimensional representation of their problem in a hierarchical way that is capable of capturing both semantic and syntactic aspects of tokens (e.g., (Bengio et al., 2006)), entities, N-grams (Wang and Manning, 2012), or phrases .