Retrieving and discovering new knowledge from documents' abstracts in scientific databases: Proposing a query-based abstractive summarization model

•Current Search Tools in Scientific Database Cannot Extract and Discover New Knowledge.•Using PRISMA Method to Retrieve and Discover New Knowledge in Scientific Database.•Proposing an Automated Model for Discovering New Knowledge in Scientific Database.•Appling QBAS Model for Discovering New Knowled...

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Vydáno v:International journal of information management data insights Ročník 5; číslo 2; s. 100366
Hlavní autoři: Abbasi Dashtaki, Neda, CheshmehSohrabi, Mehrdad, Pashootanizadeh, Mitra, Baradaran Kashani, Hamidreza
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.12.2025
Elsevier
Témata:
Document' Abstract
Information Retrieval (IR)
Knowledge Discovery (KD)
Knowledge Extraction (KE)
Knowledge Retrieval (KR)
Meta-analysis
PRISMA
Query-Based Abstractive Summarization (QBAS)
Retrieval-Augmented Generation (RAG)
Scientific databases
Systematic review
Knowledge Extraction (KE)
Scientific databases
Query-Based Abstractive Summarization (QBAS)
Knowledge Discovery (KD)
Document' Abstract
Retrieval-Augmented Generation (RAG)
Information Retrieval (IR)
Systematic review
PRISMA
Knowledge Retrieval (KR)
Meta-analysis
ISSN:2667-0968, 2667-0968
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Abstract •Current Search Tools in Scientific Database Cannot Extract and Discover New Knowledge.•Using PRISMA Method to Retrieve and Discover New Knowledge in Scientific Database.•Proposing an Automated Model for Discovering New Knowledge in Scientific Database.•Appling QBAS Model for Discovering New Knowledge in Scientific Database. Current search engines for Knowledge Retrieval (KR) and Knowledge Discovery (KD) do not effectively utilize scientifically validated documents, especially those indexed in scientific databases. Scientific databases e.g., Scopus primarily consist of document-based content and provide documents' abstract. Their Information Retrieval (IR) system only perform document searches and lack the capability to extract and discover new knowledge from documents' abstract in these databases and responding to users’ queries. The aim is to introduce a model that can efficiently perform these tasks. The statistical population for this study encompasses all scientific databases, with a particular emphasis on Scopus. To clarify the process of KR and KD as we define it, we employed a systematic review and meta-analysis framework using 33 queries. We conducted the identification, screening, eligibility, and inclusion steps following the PRISMA protocol. Next, we performed extraction, labeling, grouping, analysis, and inference. The outcome of these processes provided us with novel insights, which contribute to our exploratory knowledge. To automate these processes, we have proposed a conceptual model from query-based indirect abstractive summarization approach. The outcomes of this research offer fresh insights to database designers, administrators, and researchers, enabling the development of tools for KR and KD within these invaluable knowledge repositories. The integration of such tools into scientific databases will enhance user access to scientific knowledge to meet their informational and research needs.
AbstractList •Current Search Tools in Scientific Database Cannot Extract and Discover New Knowledge.•Using PRISMA Method to Retrieve and Discover New Knowledge in Scientific Database.•Proposing an Automated Model for Discovering New Knowledge in Scientific Database.•Appling QBAS Model for Discovering New Knowledge in Scientific Database. Current search engines for Knowledge Retrieval (KR) and Knowledge Discovery (KD) do not effectively utilize scientifically validated documents, especially those indexed in scientific databases. Scientific databases e.g., Scopus primarily consist of document-based content and provide documents' abstract. Their Information Retrieval (IR) system only perform document searches and lack the capability to extract and discover new knowledge from documents' abstract in these databases and responding to users’ queries. The aim is to introduce a model that can efficiently perform these tasks. The statistical population for this study encompasses all scientific databases, with a particular emphasis on Scopus. To clarify the process of KR and KD as we define it, we employed a systematic review and meta-analysis framework using 33 queries. We conducted the identification, screening, eligibility, and inclusion steps following the PRISMA protocol. Next, we performed extraction, labeling, grouping, analysis, and inference. The outcome of these processes provided us with novel insights, which contribute to our exploratory knowledge. To automate these processes, we have proposed a conceptual model from query-based indirect abstractive summarization approach. The outcomes of this research offer fresh insights to database designers, administrators, and researchers, enabling the development of tools for KR and KD within these invaluable knowledge repositories. The integration of such tools into scientific databases will enhance user access to scientific knowledge to meet their informational and research needs.
Current search engines for Knowledge Retrieval (KR) and Knowledge Discovery (KD) do not effectively utilize scientifically validated documents, especially those indexed in scientific databases. Scientific databases e.g., Scopus primarily consist of document-based content and provide documents' abstract. Their Information Retrieval (IR) system only perform document searches and lack the capability to extract and discover new knowledge from documents' abstract in these databases and responding to users’ queries. The aim is to introduce a model that can efficiently perform these tasks. The statistical population for this study encompasses all scientific databases, with a particular emphasis on Scopus. To clarify the process of KR and KD as we define it, we employed a systematic review and meta-analysis framework using 33 queries. We conducted the identification, screening, eligibility, and inclusion steps following the PRISMA protocol. Next, we performed extraction, labeling, grouping, analysis, and inference. The outcome of these processes provided us with novel insights, which contribute to our exploratory knowledge. To automate these processes, we have proposed a conceptual model from query-based indirect abstractive summarization approach. The outcomes of this research offer fresh insights to database designers, administrators, and researchers, enabling the development of tools for KR and KD within these invaluable knowledge repositories. The integration of such tools into scientific databases will enhance user access to scientific knowledge to meet their informational and research needs.
ArticleNumber 100366
Author Abbasi Dashtaki, Neda
CheshmehSohrabi, Mehrdad
Pashootanizadeh, Mitra
Baradaran Kashani, Hamidreza
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Issue 2
Keywords Knowledge Extraction (KE)
Scientific databases
Query-Based Abstractive Summarization (QBAS)
Knowledge Discovery (KD)
Document' Abstract
Retrieval-Augmented Generation (RAG)
Information Retrieval (IR)
Systematic review
PRISMA
Knowledge Retrieval (KR)
Meta-analysis
Language English
License This is an open access article under the CC BY-NC-ND license.
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elsevier_sciencedirect_doi_10_1016_j_jjimei_2025_100366
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PublicationDate December 2025
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2025-12-01
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PublicationTitle International journal of information management data insights
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Elsevier
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Snippet •Current Search Tools in Scientific Database Cannot Extract and Discover New Knowledge.•Using PRISMA Method to Retrieve and Discover New Knowledge in...
Current search engines for Knowledge Retrieval (KR) and Knowledge Discovery (KD) do not effectively utilize scientifically validated documents, especially...
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StartPage 100366
SubjectTerms Document' Abstract
Information Retrieval (IR)
Knowledge Discovery (KD)
Knowledge Extraction (KE)
Knowledge Retrieval (KR)
Meta-analysis
PRISMA
Query-Based Abstractive Summarization (QBAS)
Retrieval-Augmented Generation (RAG)
Scientific databases
Systematic review
Title Retrieving and discovering new knowledge from documents' abstracts in scientific databases: Proposing a query-based abstractive summarization model
URI https://dx.doi.org/10.1016/j.jjimei.2025.100366
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