The task of organizing a shuffled set of sentences into a coherent text has
been used to evaluate a machine's understanding of causal and temporal
relations. We formulate the sentence ordering task as a conditional
text-to-marker generation problem. We present Reorder-BART (Re-BART) that
leverages a pre-trained Transformer-based model to identify a coherent order
for a given set of shuffled sentences. The model takes a set of shuffled
sentences with sentence-specific markers as input and generates a sequence of
position markers of the sentences in the ordered text. Re-BART achieves the
state-of-the-art performance across 7 datasets in Perfect Match Ratio (PMR) and
Kendall's tau ($\tau$). We perform evaluations in a zero-shot setting,
showcasing that our model is able to generalize well across other datasets. We
additionally perform several experiments to understand the functioning and
limitations of our framework.

该研究提出了一种基于 Transformer 模型的 Reorder-BART 方法，旨在将文本中的乱序句子整合为有条理的文本，并在 7 个数据集上取得了较优的结果。其在零样本情况下也表现出良好的泛化能力。