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. 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.
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Molecular Biology
- Computer Science Applications
- Computational Theory and Mathematics
- Computational Mathematics