Graph-based abstractive biomedical text summarization.
Summarization is the process of compressing a text to obtain its important informative parts. In recent years, various methods have been presented to extract important parts of textual documents to present them in a summarized form. The first challenge of these methods is to detect the concepts that...
| Publicado en: | Journal of Biomedical Informatics Vol. 132 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Academic Press Inc.
Aug2022
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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=158403794&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158403794 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Aug2022 vid: 132 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 158403794 158403794 NLM35700914 158403794 10.1016/j.jbi.2022.104099 NLM35700914 158403794 ppct: 1 formats: tig: atl: Graph-based abstractive biomedical text summarization. aug: au: Givchi, Azadeh Ramezani, Reza Baraani-Dastjerdi, Ahmad affil: Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran sug: subj: Algorithms Semantics Concept Formation Software Language Human ab: Summarization is the process of compressing a text to obtain its important informative parts. In recent years, various methods have been presented to extract important parts of textual documents to present them in a summarized form. The first challenge of these methods is to detect the concepts that well convey the main topic of the text and extract sentences that better describe these essential concepts. The second challenge is the correct interpretation of the essential concepts to generate new paraphrased sentences such that they are not exactly the same as the sentences in the main text. The first challenge has been addressed by many researchers. However, the second one is still in progress. In this study, we focus on the abstractive summarization of biomedical documents. In this regard, for the first challenge, a new method is presented based on the graph generation and frequent itemset mining for generating extractive summaries by considering the concepts within the biomedical documents. Then, to address the second challenge, a transfer learning-based method is used to generate abstractive summarizations from extractive summaries. The efficiency of the proposed solution has been evaluated by conducting several experiments over BioMed Central and NLM's PubMed datasets. The obtained results show that the proposed approach admits a better interpretation of the main concepts and sentences of biomedical documents for the abstractive summarization by obtaining the overall ROUGE of 59.60%, which, on average, is 17% better than state-of-the-art summarization techniques. The source code, datasets, and results are available in GitHub1. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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