  Sentence ordering is one of important tasks in NLP. Previous works mainly
focused on improving its performance by using pair-wise strategy. However, it
is nontrivial for pair-wise models to incorporate the contextual sentence
information. In addition, error prorogation could be introduced by using the
pipeline strategy in pair-wise models. In this paper, we propose an end-to-end
neural approach to address the sentence ordering problem, which uses the
pointer network (Ptr-Net) to alleviate the error propagation problem and
utilize the whole contextual information. Experimental results show the
effectiveness of the proposed model. Source codes and dataset of this paper are
available.
