  Sentence ordering is a general and critical task for natural language
generation applications. Previous works have focused on improving its
performance in an external, downstream task, such as multi-document
summarization. Given its importance, we propose to study it as an isolated
task. We collect a large corpus of academic texts, and derive a data driven
approach to learn pairwise ordering of sentences, and validate the efficacy
with extensive experiments. Source codes and dataset of this paper will be made
publicly available.
