title: Figure-associated text summarization and evaluation.
abstract: Biomedical literature incorporates millions of figures, which are a rich and important knowledge resource for biomedical researchers. Scientists need access to the figures and the knowledge they represent in order to validate research findings and to generate new hypotheses. By themselves, these figures are nearly always incomprehensible to both humans and machines and their associated texts are therefore essential for full comprehension. The associated text of a figure, however, is scattered throughout its full-text article and contains redundant information content. In this paper, we report the continued development and evaluation of several figure summarization systems, the FigSum+ systems, that automatically identify associated texts, remove redundant information, and generate a text summary for every figure in an article. Using a set of 94 annotated figures selected from 19 different journals, we conducted an intrinsic evaluation of FigSum+. We evaluate the performance by precision, recall, F1, and ROUGE scores. The best FigSum+ system is based on an unsupervised method, achieving F1 score of 0.66 and ROUGE-1 score of 0.97. The annotated data is available at figshare.com (http://figshare.com/articles/Figure_Associated_Text_Summarization_and_Evaluation/858903).
authors: Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, United States of America.
authors: Balaji
authors: B
authors: Polepalli Ramesh
authors: Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, United States of America.
authors: Ricky J
authors: RJ
authors: Sethi
authors: Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, United States of America; School of Computer Science, University of Massachusetts, Amherst, MA, United States of America; VA Central Western Massachusetts, Leeds, MA, United States of America.
authors: Hong
authors: H
authors: Yu
mesh_terms: D035843:Biomedical Research; D003196:Computer Graphics; D057225:Data Mining / Q000379:methods*; D011642:Publications
references: BMC Bioinformatics. 2011;12:188
references: 21605399
references: Bioinformatics. 2011 Jul 1;27(13):i120-8
references: 21685060
references: BMC Bioinformatics. 2006;7:392
references: 16939640
references: Bioinformatics. 2009 Dec 1;25(23):3174-80
references: 19783830
references: Int J Med Inform. 2009 Dec;78(12):e59-67
references: 19546026
references: BMC Med Inform Decis Mak. 2011;11:6
references: 21284871
references: AMIA Annu Symp Proc. 2006;:834-8
references: 17238458
references: J Med Libr Assoc. 2010 Oct;98(4):273-81
references: 20936065
references: AMIA Annu Symp Proc. 2009;2009:6-10
references: 20351812
references: BMJ. 2012;344:e2856
references: 22563098
references: Bioinformatics. 2011 Jun 15;27(12):1723-4
references: 21493658
references: BMC Bioinformatics. 2007;8 Suppl 9:S4
references: 18047705
references: BMC Cell Biol. 2008;9:49
references: 18789160
references: PLoS One. 2011;6(8):e23862
references: 21887336
references: Int J Med Inform. 2004 Feb;73(1):1-23
references: 15036075
references: J Biomed Discov Collab. 2009 Jan 06;4:1
references: 19126221
references: Artif Intell Med. 2011 Sep;53(1):1-14
references: 21752612
references: J Am Med Inform Assoc. 1998 Jan-Feb;5(1):1-11
references: 9452981
references: BMC Bioinformatics. 2013;14:71
references: 23445074