MultiGBS: A multi-layer graph approach to biomedical summarization.
Automatic text summarization methods generate a shorter version of the input text to assist the reader in gaining a quick yet informative gist. Existing text summarization methods generally focus on a single aspect of text when selecting sentences, causing the potential loss of essential information...
| Publicado en: | Journal of Biomedical Informatics Vol. 116 |
|---|---|
| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
Academic Press Inc.
Apr2021
|
| 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=149784907&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149784907 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Apr2021 vid: 116 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 149784907 149784907 NLM33610879 10.1016/j.jbi.2021.103706 NLM33610879 149784907 ppct: 1 formats: tig: atl: MultiGBS: A multi-layer graph approach to biomedical summarization. aug: au: Davoodijam, Ensieh Ghadiri, Nasser Lotfi Shahreza, Maryam Rinaldi, Fabio affil: Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran sug: subj: Data Mining Semantics Algorithms Language Natural Language Processing ab: Automatic text summarization methods generate a shorter version of the input text to assist the reader in gaining a quick yet informative gist. Existing text summarization methods generally focus on a single aspect of text when selecting sentences, causing the potential loss of essential information. In this study, we propose a domain-specific method that models a document as a multi-layer graph to enable multiple features of the text to be processed at the same time. The features we used in this paper are word similarity, semantic similarity, and co-reference similarity, which are modelled as three different layers. The unsupervised method selects sentences from the multi-layer graph based on the MultiRank algorithm and the number of concepts. The proposed MultiGBS algorithm employs UMLS and extracts the concepts and relationships using different tools such as SemRep, MetaMap, and OGER. Extensive evaluation by ROUGE and BERTScore shows increased F-measure values. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|