Automated storytelling evaluation and story chain generation

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 multi-criteria 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 literature-based discovery and link analysis tasking. In addition, the methods provide quantitative measures of the strength of these associations.