This paper introduces the shared task of summarizing documents in several
creative domains, namely literary texts, movie scripts, and television scripts.
Summarizing these creative documents requires making complex literary
interpretations, as well as understanding non-trivial temporal dependencies in
texts containing varied styles of plot development and narrative structure.
This poses unique challenges and is yet underexplored for text summarization
systems. In this shared task, we introduce four sub-tasks and their
corresponding datasets, focusing on summarizing books, movie scripts, primetime
television scripts, and daytime soap opera scripts. We detail the process of
curating these datasets for the task, as well as the metrics used for the
evaluation of the submissions. As part of the CREATIVESUMM workshop at COLING
2022, the shared task attracted 18 submissions in total. We discuss the
submissions and the baselines for each sub-task in this paper, along with
directions for facilitating future work in the field.

介绍了在创意领域中文本摘要的共享任务，其中包括文学文本、电影剧本和电视剧本，此任务需要进行复杂的文学解释，在各种类型的情节发展和叙述结构的文本中理解非平凡的时间依赖性，共有 4 个子任务及其相应的数据集，着重于总结图书、电影剧本、黄金时段电视剧本和白天肥皂剧的故事情节，讨论了该任务的评估指标、共收到 18 份投稿以及未来工作的方向。

CREATIVESUMM：面向创意写作的自动摘要共享任务

CREATIVESUMM: Shared Task on Automatic Summarization for Creative Writing

Much work has been done in understanding human creativity and defining
measures to evaluate creativity. This is necessary mainly for the reason of
having an objective and automatic way of quantifying creative artifacts. In
this work, we propose a regression-based learning framework which takes into
account quantitatively the essential criteria for creativity like novelty,
influence, value and unexpectedness. As it is often the case with most creative
domains, there is no clear ground truth available for creativity. Our proposed
learning framework is applicable to all creative domains; yet we evaluate it on
a dataset of movies created from IMDb and Rotten Tomatoes due to availability
of audience and critic scores, which can be used as proxy ground truth labels
for creativity. We report promising results and observations from our
experiments in the following ways : 1) Correlation of creative criteria with
critic scores, 2) Improvement in movie rating prediction with inclusion of
various creative criteria, and 3) Identification of creative movies.

提出基于回归的学习框架，从创意的关键标准（如新颖、影响力、价值和意外性）的量化角度来评估创意。在电影数据集上进行实验，结果显示：1）创意标准与评论员分数的相关性，2）包含各种创意标准时可以提高电影评分预测的准确性，3）鉴别创意电影。