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Department of Systems Engineering

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https://hdl.handle.net/10217/199888
This digital collection includes faculty/student publications, theses, dissertations, and datasets from the Department of Systems Engineering.

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Browsing Department of Systems Engineering by Author "ACM, publisher"

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    ItemOpen Access
    Algorithm parallelism for improved extractive summarization
    (Colorado State University. Libraries, 2023-08-22) Villanueva, Arturo N., Jr., author; Simske, Steven J., author; ACM, publisher
    While much work on abstractive summarization has been conducted in recent years, including state-of-the-art summarizations from GPT-4, extractive summarization's lossless nature continues to provide advantages, preserving the style and often key phrases of the original text as meant by the author. Libraries for extractive summarization abound, with a wide range of efficacy. Some do not perform much better or perform even worse than random sampling of sentences extracted from the original text. This study breathes new life to using classical algorithms by proposing parallelism through an implementation of a second order meta-algorithm in the form of the Tessellation and Recombination with Expert Decisioner (T&R) pattern, taking advantage of the abundance of already-existing algorithms and dissociating their individual performance from the implementer's biases. Resulting summaries obtained using T&R are better than any of the component algorithms.
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    ItemOpen Access
    Character relationship mapping in major fictional works using text analysis methods
    (Colorado State University. Libraries, 2023-08-22) Wolyn, Sam, author; Simske, Steven, author; ACM, publisher
    Determining the relationships between characters is an important step in analyzing fictional works. Knowing character relationships can be useful when summarizing a work and may also help to determine authorship. In this paper, scores are generated for pairs of characters in fictional works, which can be used for classification tasks if characters have a relationship or not. An SVM is used to predict relationships between characters. Characters farther from the decision boundary often had stronger relationships than those closer to the boundary. The relative rank of the relationships may have additional literary and authorship related purposes.