Comparison of Extractive and Abstractive Approaches in Automatic Text Summarization: An Evaluation on BBC-News and PubMed Datasets

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dc.contributor.authorYunus, Said
dc.contributor.authorHark, Cengiz
dc.contributor.authorOkumus, Fatih
dc.date.accessioned2026-04-04T13:18:59Z
dc.date.available2026-04-04T13:18:59Z
dc.date.issued2024
dc.departmentİnönü Üniversitesi
dc.description8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024 -- 21 September 2024 through 22 September 2024 -- Malatya -- 203423
dc.description.abstractThis study focuses on the effectiveness of advanced and up-to-date text summarization techniques in the field of automatic text summarization. Among the extractive summarization systems examined for their performance are TextRank, LexRank, KL-Summ, and LSA, while the abstractive summarization systems include Pegasus, BART, T5, and LED. The capabilities of these state-of-the-art models have been evaluated on the BBC-News and PubMed datasets. Several evaluation metrics such as SacreBLEU, METEOR, BERTScore, and ROUGE were employed. Summaries of 50,100, and 150 words were generated for the BBC-News and PubMed datasets. The findings of the study provide a comprehensive evaluation of the performance of different summarization techniques across various summary lengths on the BBC-News and PubMed datasets. It was reported that the TextRank approach achieved notably successful results in text summarization. Among the abstractive methods investigated, LED demonstrated a strong ability to generate contextually accurate summaries. This study is considered to make significant contributions to the literature. © 2024 IEEE.
dc.identifier.doi10.1109/IDAP64064.2024.10710907
dc.identifier.isbn979-833153149-2
dc.identifier.scopus2-s2.0-85207939898
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/IDAP64064.2024.10710907
dc.identifier.urihttps://hdl.handle.net/11616/108054
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250329
dc.subjectabstractive summarization
dc.subjectcomparative analysis
dc.subjectextractive summarization
dc.subjecttext summarization
dc.titleComparison of Extractive and Abstractive Approaches in Automatic Text Summarization: An Evaluation on BBC-News and PubMed Datasets
dc.typeConference Object

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