Karc1 summarization: A simple and effective approach for automatic text summarization using Karc1 entropy

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dc.authoridKarci, Ali/0000-0002-8489-8617
dc.authorwosidKarci, Ali/AAG-5337-2019
dc.contributor.authorHark, Cengiz
dc.contributor.authorKarci, Ali
dc.date.accessioned2024-08-04T20:47:02Z
dc.date.available2024-08-04T20:47:02Z
dc.date.issued2020
dc.departmentİnönü Üniversitesien_US
dc.description.abstractIncreases in the amount of text resources available via the Internet has amplified the need for automated document summarizing tools. However, further efforts are needed in order to improve the quality of the existing summarization tools currently available. The current study proposes Karc1 Summarization, a novel methodology for extractive, generic summarization of text documents. Karc1 Entropy was used for the first time in a document summarization method within a unique approach. An important feature of the proposed system is that it does not require any kind of information source or training data. At the stage of presenting the input text, a tool for text processing was introduced; known as KUSH (named after its authors; Karc1, Uckan, Seyyarer, and Hark), and is used to protect semantic consistency between sentences. The Karc1 Entropy-based solution chooses the most effective, generic and most informational sentences within a paragraph or unit of text. Experimentation with the Karc1 Summarization approach was tested using openaccess document text (Document Understanding Conference; DUC-2002, DUC-2004) datasets. Performance achievement of the Karci Summarization approach was calculated using metrics known as Recall-Oriented Understudy for Gisting Evaluation (ROUGE). The experimental results showed that the proposed summarizer outperformed all current state-of-the-art methods in terms of 200-word summaries in the metrics of ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-W-1.2. In addition, the proposed summarizer outperformed the nearest competitive summarizers by a factor of 6.4% for ROUGE-1 Recall on the DUC-2002 dataset. These results demonstrate that Karci Summarization is a promising technique and it is therefore expected to attract interest from researchers in the field. Our approach was shown to have a high potential for adoptability. Moreover, the method was assessed as quite insensitive to disorderly and missing texts due to its KUSH text processing module.en_US
dc.identifier.doi10.1016/j.ipm.2019.102187
dc.identifier.issn0306-4573
dc.identifier.issn1873-5371
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85076963746en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.ipm.2019.102187
dc.identifier.urihttps://hdl.handle.net/11616/99114
dc.identifier.volume57en_US
dc.identifier.wosWOS:000528550100018en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofInformation Processing & Managementen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectKarc1 entropyen_US
dc.subjectEntropyen_US
dc.subjectGraph entropyen_US
dc.subjectGraph-based summarizationen_US
dc.subjectGeneric document summarizationen_US
dc.subjectText miningen_US
dc.titleKarc1 summarization: A simple and effective approach for automatic text summarization using Karc1 entropyen_US
dc.typeArticleen_US

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