  The task of organizing a shuffled set of sentences into a coherent text is
important in NLP and has been used to evaluate a machine's understanding of
causal and temporal relations. We present Reorder-BART (RE-BART), a sentence
ordering framework which leverages a pre-trained transformer-based model to
identify a coherent order for a given set of shuffled sentences. We reformulate
the task as a conditional text-to-marker generation setup where the input is a
set of shuffled sentences with sentence-specific markers and output is a
sequence of position markers of the ordered text. Our framework achieves the
state-of-the-art performance across six datasets in Perfect Match Ratio (PMR)
and Kendall's tau ($\tau$) metric. We perform evaluations in a zero-shot
setting, showcasing that our model is able to generalize well across other
datasets. We additionally perform a series of experiments to understand the
functioning and explore the limitations of our framework.
