MeSH: A window into full text for document summarization

Bioinformatics

Volumn 27, Issue 13, 2011, Pages

  Bhattacharya, Sanmitra ^a   Ha Thuc, Viet ^a   Srinivasan, Padmini ^a,b  

^a University of Iowa   (United States)

^b UNIVERSITY OF IOWA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; INFORMATION RETRIEVAL; MEDLINE; MESH HEADING; UNITED STATES;

INFORMATION STORAGE AND RETRIEVAL; MEDICAL SUBJECT HEADINGS; MEDLINE; UNITED STATES;

EID: 79959391641     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btr223     Document Type: Article

References (35)

1. Agarwal, S. and Yu, H. (2009) FigSum: automatically generating structured text summaries for figures in biomedical literature. AMIA Annu. Symp. Proc., 2009, -10.
2. Aone, C. et al. (1999) A trainable summarizer with knowledge acquired from robust NLP techniques. In Mani, I. and Maybury, M.T. (eds) Advances in Automatic Text Summarization, pp. 71-80.
3. Bhattacharya, S. et al. (2010a). Cross-species gene normalization at the University of Iowa. In In Proceedings of the BioCreative III workshop, Bethesda, MD, SA, pp. 55-59.
4. Bhattacharya, S. et al. (2010b) Online gene indexing and retrieval for BioCreative III at the University of Iowa. In In Proceedings of the BioCreative III orkshop, Bethesda, MD, USA, pp. 52-54.
5. Brandow, R. et al. (1995) Automatic condensation of electronic publications by sentence selection. Inf. Process. Manage, 31, 675-685.
6. Chiang, J.-H. et al. (2006) GeneLibrarian: an effective gene-information summarization and visualization system. BMC Bioinformatics, 7, 392.
7. Cohen, A.M. (2008) Five-way smoking status classification using text hot-spot identification and error-correcting output codes. J. Am. Med. Inform. Assoc., 15, 2-35.
8. Cohen, K.B. et al. (2010) The structural and content aspects of abstracts versus bodies of full text journal articles are different. BMC Bioinformatics, 11, 492.
9. Conroy, J.M. and O'leary, D.P. (2001) Text summarization via hidden Markov models. In Proceedings of the 24th Annual Iternational ACMSIGIR Conference on Research and Development in Information Retrieval, SIGIR '01, ACM, New York, NY, USA, pp. 406-407.
10. Fiszman, M. et al. (2004) Abstraction summarization for managing the biomedical research literature. In Proceedings of the HLT-NAACL Workshop on Computational Lexical Semantics, CLS '04, Association for Computational Linguistics, Morristown, NJ, USA, pp. 76-83.
11. Hersh, W. et al. (2007) TREC 2007 genomics track overview. The Sixteenth Text Retrieval Conference. Gaithersburg, MD.
12. Hersh, W. et al. (2006)TREC 2006 genomics track overview. The Fifteenth Text Retrieval Conference. Gaithersburg, MD.
13. Inouye, D. (2010) Multiple post microblog summarization. Research Final Report, University of Colorado at Colorado Springs, Colorado Springs, GA.
14. Jin, F. et al. (2009) Towards automatic generation of gene summary. In Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing, BioNLP '09, Association for Computational Linguistics, Stroudsburg, PA, USA, pp. 97-105.
15. Johnson, F.C. et al. (1997) The Application of Linguistic Processing to Automatic Abstract Generation. Morgan Kaufmann Publishers Inc., San Francisco, CA, SA.
16. Kupiec, J. et al. (1995) A trainable document summarizer. In Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '95, ACM, New York, NY, USA, pp. 68-73.
17. Lin, C.-Y. (2004) ROUGE: a package for automatic evaluation of summaries. In Moens, M.-F. and Szpakowicz,S. (eds) Text Summarization Branches Out: Proceedings of the ACL-04 Workshop, Association for Computational Linguistics, Barcelona, Spain, pp. 74-81.
18. Lin, J. (2009) Is searching full text more effective than searching abstracts? BMC Bioinformatics, 10, 46.
19. Lin, C.-Y. and Hovy, E. (2003) Automatic evaluation of summaries using N-gram cooccurrence statistics. In Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1, NAACL '03, Association for Computational inguistics, Morristown, NJ, USA, pp. 71-78.
20. Ling, X. et al. (2007) Generating gene summaries from biomedical literature: a study of semi-structured summarization. Inf. Process. Manage, 43, 1777-1791.
21. Luhn, H.P. (1958) The automatic creation of literature abstracts. IBM J. Res. Dev., 2, 159-165.
22. Radev, D.R. and McKeown, K.R. (1998) Generating natural language summaries from multiple on-line sources. Comput. Linguist., 24, 470-500.
23. Radev, D. et al. (2004a) MEAD - a platform for multidocument multilingual text summarization. In Proceedings of the 4th International Conference on Language Resources and Evaluation (LREC 2004), Lisbon, Portugal.
24. Radev, D.R. et al. (2004b) Centroid-based summarization of multiple documents. Inf. Process. Manage, 40, 919-938.
25. Radev, D. et al. (2001) Experiments in single and multidocument summarization using MEAD. In Proceedings of the Document Understanding Conference. New rleans, LA.
26. Randolph, J.J. (2005) Free-marginal multirater kappa: an alternative to Fleiss' fixedmarginal multirater kappa. In Joensuu University Learning and Instruction ymposium 2005, Joensuu, Finland.
27. Reeve, L.H. et al. (2007) Biomedical text summarisation using concept chains. Int. J. Data Min. Bioinform., 1, 389-407.
28. Reynar, J.C. and Ratnaparkhi,A. (1997) A maximum entropy approach to identifying sentence boundaries. In Proceedings of the Fifth Conference on Applied Natural Language Processing, ANLC '97, Association for Computational Linguistics, Washington, DC, pp. 16-19.
29. Salton, G. (1971) The SMART Retrieval System - Experiments in Automatic Document Processing. Prentice-Hall, Inc., Upper Saddle River, NJ, USA.
30. Sehgal, A.K. and Srinivasan, P. (2006) Retrieval with gene queries. BMC Bioinformatics, 7, 220.
31. Srinivasan, P. (2004) Text mining: generating hypotheses from MEDLINE. J. Am. Soc. Inf. Sci. Technol., 55, 396-413.
32. Srinivasan, P. and Libbus, B. (2004) Mining MEDLINE for implicit links between dietary substances and diseases. Bioinformatics, 20 (Suppl. 1), i290-i296.
33. Trieschnigg, D. et al. (2009) MeSH Up: effective MeSH text classification for improved document retrieval. Bioinformatics, 25, 1412-1418.
34. Yoo, I. et al. (2007) A coherent graph-based semantic clustering and summarization approach for biomedical literature and a new summarization evaluation ethod. BMC Bioinformatics, 8 (Suppl. 9), S4.
35. Zhu, S. et al. (2009) Enhancing MEDLINE document clustering by incorporating MeSH semantic similarity. Bioinformatics, 25, 1944-1951.