{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,7]],"date-time":"2024-09-07T18:20:57Z","timestamp":1725733257171},"reference-count":17,"publisher":"Oxford University Press (OUP)","issue":"8","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2011,4,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: The growing availability of full-text scientific articles raises the important issue of how to most efficiently digest full-text content. Although article titles and abstracts provide accurate and concise information on an article's contents, their brevity inevitably entails the loss of detail. Full-text articles provide those details, but require more time to read. The primary goal of this study is to combine the advantages of concise abstracts and detail-rich full-texts to ease the burden of reading.<\/jats:p>\n               <jats:p>Results: We retrieved abstract-related paragraphs from full-text articles through shared keywords between the abstract and paragraphs from the main text. Significant paragraphs were then recommended by applying a proposed paragraph ranking approach. Finally, the user was provided with a condensed text consisting of these significant paragraphs, allowing the user to save time from perusing the whole article. We compared the performance of the proposed approach with a keyword counting approach and a PageRank-like approach. Evaluation was conducted in two aspects: the importance of each retrieved paragraph and the information coverage of a set of retrieved paragraphs. In both evaluations, the proposed approach outperformed the other approaches.<\/jats:p>\n               <jats:p>Contact: \u00a0jchiang@mail.ncku.edu.tw<\/jats:p>","DOI":"10.1093\/bioinformatics\/btr080","type":"journal-article","created":{"date-parts":[[2011,2,18]],"date-time":"2011-02-18T01:18:15Z","timestamp":1297991895000},"page":"1143-1149","source":"Crossref","is-referenced-by-count":5,"title":["Condensing biomedical journal texts through paragraph ranking"],"prefix":"10.1093","volume":"27","author":[{"given":"Jung-Hsien","family":"Chiang","sequence":"first","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heng-Hui","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi-Ting","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2011,2,16]]},"reference":[{"key":"2023061311473857000_B1","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/S0169-7552(98)00110-X","article-title":"The anatomy of a large-scale hyper-textual Web search engine","volume":"30","author":"Brin","year":"1998","journal-title":"Comput. 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