The act of telling stories is a fundamental part of what it means to be human. This work introduces the concept of narrative information, which we define to be the overlap in information space between a story and the items that compose the story. Using contrastive learning methods, we show how modern artificial neural networks can be leveraged to distill stories and extract a representation of the narrative information. We then demonstrate how evolutionary algorithms can leverage this to extract a set of narrative templates and how these templates -- in tandem with a novel curve-fitting algorithm we introduce -- can reorder music albums to automatically induce stories in them. In the process of doing so, we give strong statistical evidence that these narrative information templates are present in existing albums. While we experiment only with music albums here, the premises of our work extend to any form of (largely) independent media.

讲故事的行为是它意味着什么的基本部分 人类。这部作品引入了叙事信息的概念，我们 定义为故事和条目之间的信息空间的重叠 这就构成了这个故事。使用对比学习方法，我们展示了现代 人工神经网络可以用来提取故事和提取一个 叙事信息的表征。然后我们演示如何 进化算法可以利用这一点来提取一组叙事 模板以及这些模板是如何与一种新的曲线拟合相结合的 我们介绍的算法--可以对音乐专辑进行重新排序，自动归纳 里面有故事。在此过程中，我们给出了很强的统计量 这些叙述性信息模板存在于现有 专辑。虽然我们在这里只尝试音乐专辑，但我们的前提是 工作扩展到任何形式的(主要)独立媒体。