Structured abstract summarization of scientific articles: Summarization using full‐text section information.
The automatic summarization of scientific articles differs from other text genres because of the structured format and longer text length. Previous approaches have focused on tackling the lengthy nature of scientific articles, aiming to improve the computational efficiency of summarizing long text u...
| Publicado en: | Journal of the Association for Information Science & Technology Vol. 74; no. 2; pp. 234 - 249 |
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| Autores principales: | , , |
| Formato: | algorithm research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Feb2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=161473777&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 161473777 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23301635 H6JN jtl: Journal of the Association for Information Science & Technology issn: 23301635 maglogo: N pubinfo: dt: Feb2023 vid: 74 iid: 2 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 161473777 160193512 161473777 161473777 10.1002/asi.24727 161473777 ppf: 234 ppct: 15 formats: tig: atl: Structured abstract summarization of scientific articles: Summarization using full‐text section information. aug: au: Oh, Hanseok Nam, Seojin Zhu, Yongjun affil: Graduate School of AI, Korea Advanced Institute of Science and Technology (KAIST), Seoul, South Korea sug: subj: Abstracts Funding Source ab: The automatic summarization of scientific articles differs from other text genres because of the structured format and longer text length. Previous approaches have focused on tackling the lengthy nature of scientific articles, aiming to improve the computational efficiency of summarizing long text using a flat, unstructured abstract. However, the structured format of scientific articles and characteristics of each section have not been fully explored, despite their importance. The lack of a sufficient investigation and discussion of various characteristics for each section and their influence on summarization results has hindered the practical use of automatic summarization for scientific articles. To provide a balanced abstract proportionally emphasizing each section of a scientific article, the community introduced the structured abstract, an abstract with distinct, labeled sections. Using this information, in this study, we aim to understand tasks ranging from data preparation to model evaluation from diverse viewpoints. Specifically, we provide a preprocessed large‐scale dataset and propose a summarization method applying the introduction, methods, results, and discussion (IMRaD) format reflecting the characteristics of each section. We also discuss the objective benchmarks and perspectives of state‐of‐the‐art algorithms and present the challenges and research directions in this area. pubtype: Academic Journal doctype: algorithm research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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