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Automated Storytelling Evaluation and Story Chain Generation

机译：自动化叙事评估和故事链生成

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Given a beginning and ending document, automated storytelling attempts to fill in intermediary documents to form a coherent story. This is a common problem for analysts; they often have two snippets of information and want to find the other pieces that relate them. Evaluation of the quality of the created stories is difficult and has routinely involved human judgment. This work extends the state of the art by providing quantitative methods of story quality evaluation which are shown to have good agreement with human judgment. Two methods of automated storytelling evaluation, dispersion and coherence are developed. Dispersion, a measure of story flow, ascertains how well the generated story flows away from the beginning document and towards the ending document. Coherence measures how well the articles in the middle of the story provide information about the relationship of the beginning and ending document pair. Kullback-Leibler divergence (KLD) is used to measure the ability to encode the vocabulary of the beginning and ending story documents using the set of middle documents in the story. The dispersion and coherence methodologies developed here have the added benefit that they do not require parametrization or user inputs and are also easily automated. An automated storytelling algorithm is proposed as a multicriteria optimization problem that maximizes dispersion and coherence simultaneously. The developed storytelling methodologies will allow for the automated identification of information which associates disparate documents in support of literaturebased discovery and link analysis tasking. In addition, the methods provide quantitative measures of the strength of these associations.
机译：对于给定的开始和结束文档，自动叙事会尝试填充中介文档以形成连贯的故事。对于分析人员来说，这是一个普遍的问题。他们通常有两个信息片段，并希望找到与它们相关的其他片段。评估所创建故事的质量非常困难，并且通常需要人工判断。通过提供定量的故事质量评估方法，这项工作扩展了现有技术水平，这些方法被证明与人类的判断具有很好的一致性。开发了两种自动叙事评估方法，即分散性和连贯性。散布度是故事流的一种度量，它确定生成的故事从起始文档流向结束文档的流向。连贯性衡量故事中间的文章提供有关开始和结束文档对之间的关​​系的信息的程度。 Kullback-Leibler散度（KLD）用于测量使用故事中的中间文档集对开始和结束故事文档的词汇进行编码的能力。这里开发的色散和相干方法具有额外的好处，即它们不需要参数化或用户输入，并且易于自动化。提出了一种自动叙事算法，作为多准则优化问题，该问题同时使分散和连贯性最大化。先进的讲故事方法将允许自动识别与不同文档相关联的信息，以支持基于文献的发现和链接分析任务。另外，这些方法提供了对这些关联强度的定量度量。

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