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Algorithm parallelism for improved extractive summarization

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dc.contributor.authorVillanueva, Arturo N., Jr., author
dc.contributor.authorSimske, Steven J., author
dc.contributor.authorACM, publisher
dc.date.accessioned2024-11-11T19:29:48Z
dc.date.available2024-11-11T19:29:48Z
dc.date.issued2023-08-22
dc.description.abstractWhile 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.
dc.format.mediumborn digital
dc.format.mediumarticles
dc.identifier.bibliographicCitationArturo N. Villanueva, Jr, and Steven J. Simske. 2023. Algorithm Parallelism for Improved Extractive Summarization. In ACM Symposium on Document Engineering 2023 (DocEng '23), August 22–25, 2023. Limerick, Ireland, 4 pages. https://doi.org/10.1145/3573128.3609350
dc.identifier.doihttps://doi.org/10.1145/3573128.3609350
dc.identifier.urihttps://hdl.handle.net/10217/239512
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartofPublications
dc.relation.ispartofACM DL Digital Library
dc.rights© Arturo N. Villanueva, Jr., et al. | ACM 2023. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in DocEng '23, https://dx.doi.org/10.1145/3573128.3609350.
dc.subjectnatural language processing
dc.subjectextractive summarization
dc.subjectmeta-algorithmics
dc.subjectmachine learning
dc.subjectdocument summarization
dc.subjecttessellation and recombination
dc.titleAlgorithm parallelism for improved extractive summarization
dc.typeText

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