To overcome the above limitation, in this work, we develop a novel deep attentive sentence ordering network (referred as ATTOrderNet) by inte- grating self-attention mechanism (Vaswani et al., 2017) with LSTMs to learn a relatively reliable paragraph representation for subsequent sentence ordering.
the bidirectional LSTM is first adopted as a sentence encoder to map the input sentences to the corresponding distributed vectors, and then a self-attention based paragraph encoder is introduced to capture structural relationships across sentences and to obtain a hierarchical context representation of the entire set of sentences.