  We present an attention-based ranking framework for learning to order
sentences given a paragraph. Our framework is built on a bidirectional sentence
encoder and a self-attention based transformer network to obtain an input order
invariant representation of paragraphs. Moreover, it allows seamless training
using a variety of ranking based loss functions, such as pointwise, pairwise,
and listwise ranking. We apply our framework on two tasks: Sentence Ordering
and Order Discrimination. Our framework outperforms various state-of-the-art
methods on these tasks on a variety of evaluation metrics. We also show that it
achieves better results when using pairwise and listwise ranking losses, rather
than the pointwise ranking loss, which suggests that incorporating relative
positions of two or more sentences in the loss function contributes to better
learning.
