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We present an attention-based ranking framework for learning to ordersentences given a paragraph. Our framework is built on a bidirectional sentenceencoder and a self-attention based transformer network to obtain an input orderinvariant representation of paragraphs. Moreover, it allows seamless trainingusing a variety of ranking based loss functions, such as pointwise, pairwise,and listwise ranking. We apply our framework on two tasks: Sentence Orderingand Order Discrimination. Our framework outperforms various state-of-the-artmethods on these tasks on a variety of evaluation metrics. We also show that itachieves better results when using pairwise and listwise ranking losses, ratherthan the pointwise ranking loss, which suggests that incorporating relativepositions of two or more sentences in the loss function contributes to betterlearning.

Deep Attentive Ranking Networks for Learning to Order Sentences