title: Condensing biomedical journal texts through paragraph ranking.
abstract: BACKGROUND
  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.
  
  
  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.
  
  
  BACKGROUND
  jchiang@mail.ncku.edu.tw.
authors.affiliation: Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan. jchiang@mail.ncku.edu.tw
authors.forename: Jung-Hsien
authors.initials: JH
authors.lastname: Chiang
authors.forename: Heng-Hui
authors.initials: HH
authors.lastname: Liu
authors.forename: Yi-Ting
authors.initials: YT
authors.lastname: Huang
mesh_terms: D000043:Abstracting and Indexing; D000465:Algorithms; D057225:Data Mining / Q000379:methods*; D010506:Periodicals as Topic