A new multi-document summarisation approach using saplings growing-up optimisation algorithms: Simultaneously optimised coverage and diversity

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dc.authoriduckan, Taner/0000-0001-5385-6775;
dc.authorwosiduckan, Taner/IZP-9705-2023
dc.authorwosidKARCI, Ali/A-9604-2019
dc.contributor.authorHark, Cengiz
dc.contributor.authorUckan, Taner
dc.contributor.authorKarci, Ali
dc.date.accessioned2024-08-04T20:52:07Z
dc.date.available2024-08-04T20:52:07Z
dc.date.issued2024
dc.departmentİnönü Üniversitesien_US
dc.description.abstractAutomatic text summarisation is obtaining a subset that accurately represents the main text. A quality summary should contain the maximum amount of information while avoiding redundant information. Redundancy is a severe deficiency that causes unnecessary repetition of information within sentences and should not occur in summarisation studies. Although many optimisation-based text summarisation methods have been proposed in recent years, there exists a lack of research on the simultaneous optimisation of scope and redundancy. In this context, this study presents an approach in which maximum coverage and minimum redundancy, which form the two key features of a rich summary, are modelled as optimisation targets. In optimisation-based text summarisation studies, different conflicting objectives are generally weighted or formulated and transformed into single-objective problems. However, this transformation can directly affect the quality of the solution. In this study, the optimisation goals are met simultaneously without transformation or formulation. In addition, the multi-objective saplings growing-up algorithm (MO-SGuA) is implemented and modified for text summarisation. The presented approach, called Pareto optimal, achieves an optimal solution with simultaneous optimisation. Experimentation with the MO-SGuA method was tested using open-access (document understanding conference; DUC) data sets. Performance success of the MO-SGuA approach was calculated using the recall-oriented understudy for gisting evaluation (ROUGE) metrics and then compared with the competitive practices used in the literature. Testing achieved a 26.6% summarisation result for the ROUGE-2 metric and 65.96% for ROUGE-L, which represents an improvement of 11.17% and 20.54%, respectively. The experimental results showed that good-quality summaries were achieved using the proposed approach.en_US
dc.identifier.doi10.1177/01655515221101841
dc.identifier.endpage650en_US
dc.identifier.issn0165-5515
dc.identifier.issn1741-6485
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85133352781en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage635en_US
dc.identifier.urihttps://doi.org/10.1177/01655515221101841
dc.identifier.urihttps://hdl.handle.net/11616/100763
dc.identifier.volume50en_US
dc.identifier.wosWOS:000822260400001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSage Publications Ltden_US
dc.relation.ispartofJournal of Information Scienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectContent coverageen_US
dc.subjectdocument summarisationen_US
dc.subjectdocument understanding conferenceen_US
dc.subjectinformation diversityen_US
dc.subjectmulti-criteria optimisationen_US
dc.subjectmulti-document summarisationen_US
dc.subjectoptimisation modelen_US
dc.subjectrecall-oriented understudy for gisting evaluationen_US
dc.subjectsaplings growing-up algorithmen_US
dc.titleA new multi-document summarisation approach using saplings growing-up optimisation algorithms: Simultaneously optimised coverage and diversityen_US
dc.typeArticleen_US

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