  Dominant sentence ordering models can be classified into pairwise ordering
models and set-to-sequence models. However, there is little attempt to combine
these two types of models, which inituitively possess complementary advantages.
In this paper, we propose a novel sentence ordering framework which introduces
two classifiers to make better use of pairwise orderings for graph-based
sentence ordering. Specially, given an initial sentence-entity graph, we first
introduce a graph-based classifier to predict pairwise orderings between linked
sentences. Then, in an iterative manner, based on the graph updated by
previously predicted high-confident pairwise orderings, another classifier is
used to predict the remaining uncertain pairwise orderings. At last, we adapt a
GRN-based sentence ordering model on the basis of final graph. Experiments on
five commonly-used datasets demonstrate the effectiveness and generality of our
model. Particularly, when equipped with BERT and FHDecoder, our model achieves
state-of-the-art performance.
