Degree centrality for semantic abstraction summarization of therapeutic studies

Journal of Biomedical Informatics

Volumn 44, Issue 5, 2011, Pages 830-838

  Zhang, Han ^a,b   Fiszman, Marcelo ^b   Shin, Dongwook ^b   Miller, Christopher M ^b   Rosemblat, Graciela ^b   Rindflesch, Thomas C ^b  

^a CHINA MEDICAL UNIVERSITY   (China)

^b NATIONAL INSTITUTES OF HEALTH   (United States)

Author keywords

Automatic summarization; Degree centrality; Disease treatment; Graph theory; Natural language processing; Semantic processing

Indexed keywords

AUTOMATIC SUMMARIZATION; DEGREE CENTRALITY; DISEASE TREATMENT; NATURAL LANGUAGE PROCESSING; SEMANTIC PROCESSING;

COMPUTATIONAL LINGUISTICS; DATABASE SYSTEMS; DISEASES; GRAPH THEORY; INFORMATION RETRIEVAL; INFORMATION RETRIEVAL SYSTEMS; SEARCH ENGINES; SEMANTICS;

NATURAL LANGUAGE PROCESSING SYSTEMS;

ALZHEIMER DISEASE; ARTICLE; BRAIN DEPTH STIMULATION; CITATION ANALYSIS; CLINICAL EVALUATION; COMORBIDITY; HEART FAILURE; HUMAN; MEDICAL INFORMATION SYSTEM; MEDLINE; MELANOMA; MIGRAINE; PALLIDOTOMY; PARKINSON DISEASE; PEPTIC ULCER; PRIORITY JOURNAL; SEMANTIC ABSTRACTION SUMMARIZATION; SEMANTICS; SUBSTITUTION THERAPY; SUPPLEMENTATION; THALAMOTOMY; TRANSCRANIAL MAGNETIC STIMULATION;

ALGORITHMS; HUMANS; INFORMATION STORAGE AND RETRIEVAL; MEDLINE; NATURAL LANGUAGE PROCESSING; SEMANTICS; UNIFIED MEDICAL LANGUAGE SYSTEM;

EID: 80052915220     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2011.05.001     Document Type: Article

References (51)

1. Fiszman M., Demner-Fushman D., Kilicoglu H., Rindflesch T.C. Automatic summarization of MEDLINE citations for evidence-based medical treatment: a topic-oriented evaluation. J Biomed Inform 2009, 42(5):801-813.
2. Sparck Jones K. Automatic summarizing: factors and directions. Advances in automatic text summarization 1999, 1-12. The MIT Press, London. I. Mani, M.T. Maybury (Eds.).
3. Marcu D. The theory and practice of discourse parsing and summarization 2000, The MIT Press, London.
4. Jensen T.K., Laegreid A., Komorowski J., Hovig A literature network of human genes for high-throughput analysis of gene expression. Nat Genet 2001, 28(1):21-28.
5. Plake C., Schiemann T., Pankalla M., Hakenberg J., Leser U. ALIBABA: PubMed as a graph. Bioinformatics 2006, 22(19):2444-2445.
6. Feldman R., Regev Y., Hurvitz E., Finkelstein-Landau M. Mining the biomedical literature using semantic analysis and natural language processing techniques. Biosilico 2003, 1(2):69-80.
7. Fiszman M, Rindflesch TC, Kilicoglu H. Abstraction summarization for managing the biomedical research literature. In: Proceedings of the HLT-NAACL workshop on computational lexical semantics; 2004. p. 76-83.
8. Rindflesch T.C., Fiszman M., Libbus B. Semantic interpretation for the biomedical research literature. Medical informatics: knowledge management and data mining in biomedicine 2005, 399-422. Springer, New York. H. Chen, S.S. Fuller, C. Friedman, W. Hersh (Eds.).
9. Kilicoglu H, Fiszman M, Rodriguez A, Shin D, Ripple AM, Rindflesch TC. Semantic MEDLINE: a web application to manage the results of PubMed searches. In: Proceedings of the third international symposium for semantic mining in biomedicine; 2008. p. 69-76.
10. Stein G.S., Strzalkowski T., Wise G.B. Interactive, text-based summarization of multiple documents. Comput Intell 2000, 16(4):606-613.
11. Humphreys B.L., Lindberg D.A., Schoolman H.M., Barnett G.O. The unified medical language system: an informatics research collaboration. J Am Med Inform Assoc 1998, 5(1):1-11.
12. Bodenreider O. The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res 2004, 32(Database issue):D267-270.
13. McCray A.T., Burgun A., Bodenreider O. Aggregating UMLS semantic types for reducing conceptual complexity. Proc Medinfo 2001, 10(Pt 1):216-220.
14. McCray AT, Srinivasan S, Browne AC. Lexical methods for managing variation in biomedical terminologies. Symposium on computer applications in medicine; 1994. p. 235-9.
15. Smith L.H., Rindflesch T.C., Wilbur W.J. The importance of the lexicon in tagging biological text. Nat Lang Eng 2005, 12(4):335-351.
16. Aronson A.R., Lang F.M. An overview of MetaMap: historical perspective and recent advances. J Am Med Inform Assoc 2010, 17(3):229-236.
17. Humphrey S.M., Rogers W.J., Kilicoglu H., Demner-Fushman D., Rindflesch T.C. Word sense disambiguation by selecting the best semantic type based on journal descriptor indexing: preliminary experiment. J Am Soc Inf Sci Technol 2006, 57(1):96-113.
18. Johnson D.B., Zou Q., Dionisio J.D., Liu V.Z., Chu W.W. Modeling medical content for automated summarization. Ann N Y Acad Sci 2002, 980:247-258.
19. Reeve L, Han H, Nagori S, Yang JC, Schwimmer TA, Brooks AD. Concept frequency distribution in biomedical text summarization. In: Proceedings of the 15th ACM international conference on information and knowledge management; 2006. p. 604-11.
20. Reeve L, Han H, Brooks AD. Biochain: lexical chaining methods for biomedical text summarization. The 21st annual ACM symposium on applied computing; 2006. p. 23-7.
21. Reeve L., Han H. Biomedical text summarisation using concept chains. Int J Data Min Bioinform 2007, 1(4):389-407.
22. Demner-Fushman D, Lin J. Answer extraction, semantic clustering, and extractive summarization for clinical question answering. In: Proceedings of the 21st international conference on computational linguistics and the 44th annual meeting of the association for computational linguistics; 2006. p. 841-8.
23. Hahn U., Mani I. The challenges of automatic summarization. IEEE Comput 2000, 33(11):29-36.
24. Afantenos S., Karkaletsis V., Stamatopoulos P. Summarization from medical document: a survey. Artif Intell Med 2005, 33(2):157-177.
25. Reichert D, Kaufman D, Bloxham B, Chase H, Elhadad N. Cognitive analysis of the summarization of longitudinal patient records. In: AMIA annu symp proc; 2010. p. 667-71.
26. Vleck TV, Elhadad N. Corpus-based problem selection for EHR note summarization. In: AMIA annu symp proc; 2010. p. 817-21.
27. Yu H. Towards answering biological questions with experimental evidence: automatically identifying text that summarize image content in full-text articles. In: AMIA annu symp proc; 2006. p. 834-8.
28. Yang J, Cohen AM, Hersh W. Automatic summarization of mouse gene information by clustering and sentence extraction from MEDLINE abstracts. In: AMIA annu symp proc; 2007. p. 831-5.
29. Fiszman M., Rindflesch T.C., Kilicoglu H. Summarization of an online medical Encyclopedia. MedInfo 2004, 506-510.
30. Fiszman M, Rindflesch TC, Kilicoglu H. Summarizing drug information in medline citations. In: AMIA annu symp proc; 2006. p. 254-8.
31. Ahlers C., Fiszman M., Demner-Fushman D., Lang F.M., Rindflesch T.C. Extracting semantic predications from MEDLINE citations for pharmacogenomics. Pac Symp Biocomput 2007, 12:209-220.
32. Workman T.E., Fiszman M., Hurdle J.F., Rindflesch T.C. Biomedical text summarization to support genetic database curation: using semantic MEDLINE to create a secondary database of genetic information. J Med Libr Assoc 2010, 98(4):273-281.
33. Hahn U., Reimer U. Knowledge-based text summarization: salience and generalization operators for knowledge base abstraction. Advances in automatic text summarization 1999, 215-232. MIT Press, Cambridge. I. Mani, M.T. Maybury (Eds.).
34. Stein GC, Bagga A, Wise GB. Multi-document summarization: methodologies and evaluations. In: Proceedings of the 7th conference on automatic natural language processing; 2000. p. 337-46.
35. Erkan G., Radev D.R. LexRank: graph-based centrality as salience in text summarization. J Artif Intell Res 2004, 22:457-479.
36. Leskovec J, Grobelnik M, Milic-frayling N. Learning sub-structures of document semantic graphs for document summarization. Workshop on link analysis and group detection; 2004.
37. Yoo I., Hu X., Song I. A coherent graph-based semantic clustering and summarization approach for biomedical literature and a new summarization evaluation method. BMC Bioinform 2007, 8(Suppl. 9):S4.
38. Chen Z, Ji H. Graph-based clustering for computational linguistics: a survey. In: Proceedings of the 2010 workshop on graph-based methods for natural language processing. ACL; 2010. p. 1-9.
39. Plaza L, Stevenson M, Diaz A. Improving summarization of biomedical documents using word sense disambiguation. In: Proceedings of the 2010 workshop on biomedical natural language processing. ACL; 2010. p. 55-63.
40. Chali Y, Hasan SA, Joty SR. Improving graph-based random walks for complex question answering using syntactic, shallow semantic and extended string subsequence kernels. Inf Process Manage. doi:10.1016/j.ipm.2010.10.002. doi:10.1016/j.ipm.2010.10.002.
41. Zhang X, Cheng G, Qu Y. Ontology summarization based on RDF sentence graph. In: Proceedings of the 16th international conference on world wide web; 2007. p. 707-16.
42. Nooy W., Mrvar A., Batagelj V. Exploratory social network analysis with Pajek 2005, Cambridge University Press, New York.
43. Jing H, Barzilay R, McKeown K, Elhadad M. Summarization evaluation methods: experiments and analysis. AAAI symposium on intelligent summarization; 1998. p. 60-8.
44. Lin CY, Hovy E. Manual and automatic evaluation of summaries. In: Proceedings of the ACL workshop on automatic summarization; 2002. p. 45-51.
45. Radev D, Teufel S, Saggion H, et al. Evaluation challenges in large-scale document summarization. In: Proceedings of the 41st annual meeting on association for computational linguistics; 2003. p. 375-82.
46. Elhadad N, McKeown K, Kaufman D, Jordan D. Facilitating physicians' access to information via tailored text summarization. In: AMIA annu symp proc; 2005. p. 226-30.
47. McKeown H, Passonneau RJ, Elson DK, et al. Do summaries help? A task-based evaluation of multidocument summarization. In: Proceedings of the 28th annual international ACM SIGIR conference on research and development in information retrieval; 2005. p. 210-17.
48. Amigo E, Gonzalo J, Peinado V. An empirical study of information synthesis tasks. In: Proceedings of the 42nd annual meeting of the association for computational linguistics; 2004. p. 207-14.
49. http://duc.nist.gov/duc2005/tasks.html.
50. http://www.accessmedicine.com/resourceTOC.aspx?resourceID=4.
51. http://www.accessmedicine.com/resourceTOC.aspx?resourceID=1.