Many implicit inferences exist in text depending on how it is structured that
can critically impact the text's interpretation and meaning. One such
structural aspect present in text with chronology is the order of its
presentation. For narratives or stories, this is known as the narrative order.
Reordering a narrative can impact the temporal, causal, event-based, and other
inferences readers draw from it, which in turn can have strong effects both on
its interpretation and interestingness. In this paper, we propose and
investigate the task of Narrative Reordering (NAREOR) which involves rewriting
a given story in a different narrative order while preserving its plot. We
present a dataset, NAREORC, with human rewritings of stories within ROCStories
in non-linear orders, and conduct a detailed analysis of it. Further, we
propose novel task-specific training methods with suitable evaluation metrics.
We perform experiments on NAREORC using state-of-the-art models such as BART
and T5 and conduct extensive automatic and human evaluations. We demonstrate
that although our models can perform decently, NAREOR is a challenging task
with potential for further exploration. We also investigate two applications of
NAREOR: generation of more interesting variations of stories and serving as
adversarial sets for temporal/event-related tasks, besides discussing other
prospective ones, such as for pedagogical setups related to language skills
like essay writing and applications to medicine involving clinical narratives.

本文提出和探讨了一项名为 “NAREOR” 的任务，即重写给定故事的叙述顺序，同时保留其情节。通过使用如 BART 和 T5 等最先进的模型，作者以非线性顺序重写了 ROCStories 中的故事，这是一个具有挑战性的任务，可用于创造更有趣的故事变体和作为时间 / 事件相关任务的对抗集。此外，该任务还有许多其他潜在的应用领域，例如语言技能教学和医学临床故事。