摘要：As an important text coherence modeling task; sentence ordering aims to coherently organize a given set of unordered sentences. To achieve this goal; the most important step is to effectively capture and exploit global dependencies among these sentences. In this paper; we propose a novel and flexible external knowledge enhanced graph-based neural network for sentence ordering. Specifically; we first represent the input sentences as a graph; where various kinds of relations (i.e.; entity-entity; sentence-sentence and entity-sentence) are exploited to make the graph representation more expressive and less noisy. Then; we introduce graph recurrent network to learn semantic representations of the sentences. To demonstrate the effectiveness of our model; we conduct experiments on several benchmark datasets. The experimental results and in-depth analysis show our model significantly outperforms the existing state-of-the-art models.
