MeSH: a window into full text for document summarization
Motivation: Previous research in the biomedical text-mining domain has historically been limited to titles, abstracts and metadata available in MEDLINE records. Recent research initiatives such as TREC Genomics and BioCreAtIvE strongly point to the merits of moving beyond abstracts and into the real...
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| Published in | Bioinformatics Vol. 27; no. 13; pp. i120 - i128 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Oxford University Press
01.07.2011
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Motivation: Previous research in the biomedical text-mining domain has historically been limited to titles, abstracts and metadata available in MEDLINE records. Recent research initiatives such as TREC Genomics and BioCreAtIvE strongly point to the merits of moving beyond abstracts and into the realm of full texts. Full texts are, however, more expensive to process not only in terms of resources needed but also in terms of accuracy. Since full texts contain embellishments that elaborate, contextualize, contrast, supplement, etc., there is greater risk for false positives. Motivated by this, we explore an approach that offers a compromise between the extremes of abstracts and full texts. Specifically, we create reduced versions of full text documents that contain only important portions. In the long-term, our goal is to explore the use of such summaries for functions such as document retrieval and information extraction. Here, we focus on designing summarization strategies. In particular, we explore the use of MeSH terms, manually assigned to documents by trained annotators, as clues to select important text segments from the full text documents.
Results: Our experiments confirm the ability of our approach to pick the important text portions. Using the ROUGE measures for evaluation, we were able to achieve maximum ROUGE-1, ROUGE-2 and ROUGE-SU4 F-scores of 0.4150, 0.1435 and 0.1782, respectively, for our MeSH term-based method versus the maximum baseline scores of 0.3815, 0.1353 and 0.1428, respectively. Using a MeSH profile-based strategy, we were able to achieve maximum ROUGE F-scores of 0.4320, 0.1497 and 0.1887, respectively. Human evaluation of the baselines and our proposed strategies further corroborates the ability of our method to select important sentences from the full texts.
Contact:
sanmitra-bhattacharya@uiowa.edu; padmini-srinivasan@uiowa.edu |
|---|---|
| AbstractList | Previous research in the biomedical text-mining domain has historically been limited to titles, abstracts and metadata available in MEDLINE records. Recent research initiatives such as TREC Genomics and BioCreAtIvE strongly point to the merits of moving beyond abstracts and into the realm of full texts. Full texts are, however, more expensive to process not only in terms of resources needed but also in terms of accuracy. Since full texts contain embellishments that elaborate, contextualize, contrast, supplement, etc., there is greater risk for false positives. Motivated by this, we explore an approach that offers a compromise between the extremes of abstracts and full texts. Specifically, we create reduced versions of full text documents that contain only important portions. In the long-term, our goal is to explore the use of such summaries for functions such as document retrieval and information extraction. Here, we focus on designing summarization strategies. In particular, we explore the use of MeSH terms, manually assigned to documents by trained annotators, as clues to select important text segments from the full text documents.
Our experiments confirm the ability of our approach to pick the important text portions. Using the ROUGE measures for evaluation, we were able to achieve maximum ROUGE-1, ROUGE-2 and ROUGE-SU4 F-scores of 0.4150, 0.1435 and 0.1782, respectively, for our MeSH term-based method versus the maximum baseline scores of 0.3815, 0.1353 and 0.1428, respectively. Using a MeSH profile-based strategy, we were able to achieve maximum ROUGE F-scores of 0.4320, 0.1497 and 0.1887, respectively. Human evaluation of the baselines and our proposed strategies further corroborates the ability of our method to select important sentences from the full texts.
sanmitra-bhattacharya@uiowa.edu; padmini-srinivasan@uiowa.edu. Motivation: Previous research in the biomedical text-mining domain has historically been limited to titles, abstracts and metadata available in MEDLINE records. Recent research initiatives such as TREC Genomics and BioCreAtIvE strongly point to the merits of moving beyond abstracts and into the realm of full texts. Full texts are, however, more expensive to process not only in terms of resources needed but also in terms of accuracy. Since full texts contain embellishments that elaborate, contextualize, contrast, supplement, etc., there is greater risk for false positives. Motivated by this, we explore an approach that offers a compromise between the extremes of abstracts and full texts. Specifically, we create reduced versions of full text documents that contain only important portions. In the long-term, our goal is to explore the use of such summaries for functions such as document retrieval and information extraction. Here, we focus on designing summarization strategies. In particular, we explore the use of MeSH terms, manually assigned to documents by trained annotators, as clues to select important text segments from the full text documents. Results: Our experiments confirm the ability of our approach to pick the important text portions. Using the ROUGE measures for evaluation, we were able to achieve maximum ROUGE-1, ROUGE-2 and ROUGE-SU4 F-scores of 0.4150, 0.1435 and 0.1782, respectively, for our MeSH term-based method versus the maximum baseline scores of 0.3815, 0.1353 and 0.1428, respectively. Using a MeSH profile-based strategy, we were able to achieve maximum ROUGE F-scores of 0.4320, 0.1497 and 0.1887, respectively. Human evaluation of the baselines and our proposed strategies further corroborates the ability of our method to select important sentences from the full texts. Contact: sanmitra-bhattacharya@uiowa.edu; padmini-srinivasan@uiowa.edu MOTIVATIONPrevious research in the biomedical text-mining domain has historically been limited to titles, abstracts and metadata available in MEDLINE records. Recent research initiatives such as TREC Genomics and BioCreAtIvE strongly point to the merits of moving beyond abstracts and into the realm of full texts. Full texts are, however, more expensive to process not only in terms of resources needed but also in terms of accuracy. Since full texts contain embellishments that elaborate, contextualize, contrast, supplement, etc., there is greater risk for false positives. Motivated by this, we explore an approach that offers a compromise between the extremes of abstracts and full texts. Specifically, we create reduced versions of full text documents that contain only important portions. In the long-term, our goal is to explore the use of such summaries for functions such as document retrieval and information extraction. Here, we focus on designing summarization strategies. In particular, we explore the use of MeSH terms, manually assigned to documents by trained annotators, as clues to select important text segments from the full text documents. RESULTSOur experiments confirm the ability of our approach to pick the important text portions. Using the ROUGE measures for evaluation, we were able to achieve maximum ROUGE-1, ROUGE-2 and ROUGE-SU4 F-scores of 0.4150, 0.1435 and 0.1782, respectively, for our MeSH term-based method versus the maximum baseline scores of 0.3815, 0.1353 and 0.1428, respectively. Using a MeSH profile-based strategy, we were able to achieve maximum ROUGE F-scores of 0.4320, 0.1497 and 0.1887, respectively. Human evaluation of the baselines and our proposed strategies further corroborates the ability of our method to select important sentences from the full texts. CONTACTsanmitra-bhattacharya@uiowa.edu; padmini-srinivasan@uiowa.edu. Motivation: Previous research in the biomedical text-mining domain has historically been limited to titles, abstracts and metadata available in MEDLINE records. Recent research initiatives such as TREC Genomics and BioCreAtIvE strongly point to the merits of moving beyond abstracts and into the realm of full texts. Full texts are, however, more expensive to process not only in terms of resources needed but also in terms of accuracy. Since full texts contain embellishments that elaborate, contextualize, contrast, supplement, etc., there is greater risk for false positives. Motivated by this, we explore an approach that offers a compromise between the extremes of abstracts and full texts. Specifically, we create reduced versions of full text documents that contain only important portions. In the long-term, our goal is to explore the use of such summaries for functions such as document retrieval and information extraction. Here, we focus on designing summarization strategies. In particular, we explore the use of MeSH terms, manually assigned to documents by trained annotators, as clues to select important text segments from the full text documents. Results: Our experiments confirm the ability of our approach to pick the important text portions. Using the ROUGE measures for evaluation, we were able to achieve maximum ROUGE-1, ROUGE-2 and ROUGE-SU4 F-scores of 0.4150, 0.1435 and 0.1782, respectively, for our MeSH term-based method versus the maximum baseline scores of 0.3815, 0.1353 and 0.1428, respectively. Using a MeSH profile-based strategy, we were able to achieve maximum ROUGE F-scores of 0.4320, 0.1497 and 0.1887, respectively. Human evaluation of the baselines and our proposed strategies further corroborates the ability of our method to select important sentences from the full texts. Contact: sanmitra-bhattacharya@uiowa.edu; padmini-srinivasan@uiowa.edu Motivation: Previous research in the biomedical text-mining domain has historically been limited to titles, abstracts and metadata available in MEDLINE records. Recent research initiatives such as TREC Genomics and BioCreAtIvE strongly point to the merits of moving beyond abstracts and into the realm of full texts. Full texts are, however, more expensive to process not only in terms of resources needed but also in terms of accuracy. Since full texts contain embellishments that elaborate, contextualize, contrast, supplement, etc., there is greater risk for false positives. Motivated by this, we explore an approach that offers a compromise between the extremes of abstracts and full texts. Specifically, we create reduced versions of full text documents that contain only important portions. In the long-term, our goal is to explore the use of such summaries for functions such as document retrieval and information extraction. Here, we focus on designing summarization strategies. In particular, we explore the use of MeSH terms, manually assigned to documents by trained annotators, as clues to select important text segments from the full text documents. Results: Our experiments confirm the ability of our approach to pick the important text portions. Using the ROUGE measures for evaluation, we were able to achieve maximum ROUGE-1, ROUGE-2 and ROUGE-SU4 F -scores of 0.4150, 0.1435 and 0.1782, respectively, for our MeSH term-based method versus the maximum baseline scores of 0.3815, 0.1353 and 0.1428, respectively. Using a MeSH profile-based strategy, we were able to achieve maximum ROUGE F -scores of 0.4320, 0.1497 and 0.1887, respectively. Human evaluation of the baselines and our proposed strategies further corroborates the ability of our method to select important sentences from the full texts. Contact: sanmitra-bhattacharya@uiowa.edu ; padmini-srinivasan@uiowa.edu |
| Author | Ha−Thuc, Viet Bhattacharya, Sanmitra Srinivasan, Padmini |
| AuthorAffiliation | 1 Department of Computer Science and 2 Department of Management Sciences, The University of Iowa, Iowa City, IA 52242, USA |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/21685060$$D View this record in MEDLINE/PubMed |
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| References | 16939640 - BMC Bioinformatics. 2006;7:392 17947623 - J Am Med Inform Assoc. 2008 Jan-Feb;15(1):32-5 20920264 - BMC Bioinformatics. 2010;11:492 15262811 - Bioinformatics. 2004 Aug 4;20 Suppl 1:i290-6 19376821 - Bioinformatics. 2009 Jun 1;25(11):1412-8 19497938 - Bioinformatics. 2009 Aug 1;25(15):1944-51 20351812 - AMIA Annu Symp Proc. 2009;2009:6-10 18047705 - BMC Bioinformatics. 2007;8 Suppl 9:S4 16630348 - BMC Bioinformatics. 2006;7:220 19192280 - BMC Bioinformatics. 2009;10:46 18402049 - Int J Data Min Bioinform. 2007;1(4):389-407 Kupiec (2023012512015777500_B16) 1995 Radev (2023012512015777500_B25) 2001 Jin (2023012512015777500_B14) 2009 Randolph (2023012512015777500_B26) 2005 Bhattacharya (2023012512015777500_B4) 2010 Cohen (2023012512015777500_B7) 2008; 15 Lin (2023012512015777500_B19) 2003 Brandow (2023012512015777500_B5) 1995; 31 Hersh (2023012512015777500_B12) 2006 Radev (2023012512015777500_B24) 2004; 40 Cohen (2023012512015777500_B8) 2010; 11 Radev (2023012512015777500_B23) 2004 Lin (2023012512015777500_B17) 2004 Salton (2023012512015777500_B29) 1971 Johnson (2023012512015777500_B15) 1997 Aone (2023012512015777500_B2) 1999 Luhn (2023012512015777500_B21) 1958; 2 Zhu (2023012512015777500_B35) 2009; 25 Sehgal (2023012512015777500_B30) 2006; 7 Bhattacharya (2023012512015777500_B3) 2010 Fiszman (2023012512015777500_B10) 2004 Srinivasan (2023012512015777500_B32) 2004; 20 Chiang (2023012512015777500_B6) 2006; 7 Conroy (2023012512015777500_B9) 2001 Inouye (2023012512015777500_B13) 2010 Srinivasan (2023012512015777500_B31) 2004; 55 Trieschnigg (2023012512015777500_B33) 2009; 25 Agarwal (2023012512015777500_B1) 2009; 2009 Lin (2023012512015777500_B18) 2009; 10 Hersh (2023012512015777500_B11) 2007 Yoo (2023012512015777500_B34) 2007; 8 Ling (2023012512015777500_B20) 2007; 43 Radev (2023012512015777500_B22) 1998; 24 Reeve (2023012512015777500_B27) 2007; 1 Reynar (2023012512015777500_B28) 1997 |
| References_xml | – start-page: 71 volume-title: Advances in Automatic Text Summarization year: 1999 ident: 2023012512015777500_B2 article-title: A trainable summarizer with knowledge acquired from robust NLP techniques contributor: fullname: Aone – volume: 43 start-page: 1777 year: 2007 ident: 2023012512015777500_B20 article-title: Generating gene summaries from biomedical literature: a study of semi-structured summarization publication-title: Inf. Process. Manage doi: 10.1016/j.ipm.2007.01.018 contributor: fullname: Ling – volume: 40 start-page: 919 year: 2004 ident: 2023012512015777500_B24 article-title: Centroid-based summarization of multiple documents publication-title: Inf. Process. Manage doi: 10.1016/j.ipm.2003.10.006 contributor: fullname: Radev – volume: 25 start-page: 1944 year: 2009 ident: 2023012512015777500_B35 article-title: Enhancing MEDLINE document clustering by incorporating MeSH semantic similarity publication-title: Bioinformatics doi: 10.1093/bioinformatics/btp338 contributor: fullname: Zhu – volume: 2 start-page: 159 year: 1958 ident: 2023012512015777500_B21 article-title: The automatic creation of literature abstracts publication-title: IBM J. Res. Dev. doi: 10.1147/rd.22.0159 contributor: fullname: Luhn – start-page: 16 volume-title: Proceedings of the Fifth Conference on Applied Natural Language Processing, ANLC '97 year: 1997 ident: 2023012512015777500_B28 article-title: A maximum entropy approach to identifying sentence boundaries doi: 10.3115/974557.974561 contributor: fullname: Reynar – start-page: 55 volume-title: In Proceedings of the BioCreative III workshop year: 2010 ident: 2023012512015777500_B3 article-title: Cross-species gene normalization at the University of Iowa contributor: fullname: Bhattacharya – volume: 2009 start-page: 6 year: 2009 ident: 2023012512015777500_B1 article-title: FigSum: automatically generating structured text summaries for figures in biomedical literature publication-title: AMIA Annu. Symp. Proc. contributor: fullname: Agarwal – volume-title: The Application of Linguistic Processing to Automatic Abstract Generation year: 1997 ident: 2023012512015777500_B15 contributor: fullname: Johnson – start-page: 68 volume-title: Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '95 year: 1995 ident: 2023012512015777500_B16 article-title: A trainable document summarizer contributor: fullname: Kupiec – volume: 31 start-page: 675 year: 1995 ident: 2023012512015777500_B5 article-title: Automatic condensation of electronic publications by sentence selection publication-title: Inf. Process. Manage doi: 10.1016/0306-4573(95)00052-I contributor: fullname: Brandow – volume-title: Proceedings of the 4th International Conference on Language Resources and Evaluation (LREC 2004) year: 2004 ident: 2023012512015777500_B23 article-title: MEAD - a platform for multidocument multilingual text summarization contributor: fullname: Radev – volume-title: Joensuu University Learning and Instruction Symposium 2005 year: 2005 ident: 2023012512015777500_B26 article-title: Free-marginal multirater kappa: an alternative to Fleiss' fixed-marginal multirater kappa contributor: fullname: Randolph – start-page: 406 volume-title: Proceedings of the 24th Annual Iternational ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '01 year: 2001 ident: 2023012512015777500_B9 article-title: Text summarization via hidden Markov models doi: 10.1145/383952.384042 contributor: fullname: Conroy – volume-title: Research Final Report year: 2010 ident: 2023012512015777500_B13 article-title: Multiple post microblog summarization contributor: fullname: Inouye – volume: 10 start-page: 46 year: 2009 ident: 2023012512015777500_B18 article-title: Is searching full text more effective than searching abstracts? publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-10-46 contributor: fullname: Lin – start-page: 71 volume-title: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1, NAACL '03 year: 2003 ident: 2023012512015777500_B19 article-title: Automatic evaluation of summaries using N-gram co-occurrence statistics contributor: fullname: Lin – start-page: 74 volume-title: Text Summarization Branches Out: Proceedings of the ACL-04 Workshop year: 2004 ident: 2023012512015777500_B17 article-title: ROUGE: a package for automatic evaluation of summaries contributor: fullname: Lin – volume: 7 start-page: 220 year: 2006 ident: 2023012512015777500_B30 article-title: Retrieval with gene queries publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-7-220 contributor: fullname: Sehgal – volume: 25 start-page: 1412 year: 2009 ident: 2023012512015777500_B33 article-title: MeSH Up: effective MeSH text classification for improved document retrieval publication-title: Bioinformatics doi: 10.1093/bioinformatics/btp249 contributor: fullname: Trieschnigg – volume-title: The SMART Retrieval System – Experiments in Automatic Document Processing. year: 1971 ident: 2023012512015777500_B29 contributor: fullname: Salton – volume-title: TREC 2007 genomics track overview. year: 2007 ident: 2023012512015777500_B11 article-title: TREC 2007 genomics track overview contributor: fullname: Hersh – volume-title: TREC 2006 genomics track overview. year: 2006 ident: 2023012512015777500_B12 article-title: TREC 2006 genomics track overview contributor: fullname: Hersh – start-page: 76 volume-title: Proceedings of the HLT-NAACL Workshop on Computational Lexical Semantics, CLS '04 year: 2004 ident: 2023012512015777500_B10 article-title: Abstraction summarization for managing the biomedical research literature doi: 10.3115/1596431.1596442 contributor: fullname: Fiszman – volume: 24 start-page: 470 year: 1998 ident: 2023012512015777500_B22 article-title: Generating natural language summaries from multiple on-line sources publication-title: Comput. Linguist. contributor: fullname: Radev – volume: 20 start-page: i290–i296 issue: l. 1 year: 2004 ident: 2023012512015777500_B32 article-title: Mining MEDLINE for implicit links between dietary substances and diseases publication-title: Bioinformatics contributor: fullname: Srinivasan – volume: 7 start-page: 392 year: 2006 ident: 2023012512015777500_B6 article-title: GeneLibrarian: an effective gene-information summarization and visualization system publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-7-392 contributor: fullname: Chiang – volume: 11 start-page: 492 year: 2010 ident: 2023012512015777500_B8 article-title: The structural and content aspects of abstracts versus bodies of full text journal articles are different publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-11-492 contributor: fullname: Cohen – start-page: 52 volume-title: Proceedings of the BioCreative III workshop year: 2010 ident: 2023012512015777500_B4 article-title: Online gene indexing and retrieval for BioCreative III at the University of Iowa contributor: fullname: Bhattacharya – volume: 1 start-page: 389 year: 2007 ident: 2023012512015777500_B27 article-title: Biomedical text summarisation using concept chains publication-title: Int. J. Data Min. Bioinform. doi: 10.1504/IJDMB.2007.012967 contributor: fullname: Reeve – volume: 55 start-page: 396 year: 2004 ident: 2023012512015777500_B31 article-title: Text mining: generating hypotheses from MEDLINE publication-title: J. Am. Soc. Inf. Sci. Technol. doi: 10.1002/asi.10389 contributor: fullname: Srinivasan – volume: 8 start-page: S4 issue: Suppl. 9 year: 2007 ident: 2023012512015777500_B34 article-title: A coherent graph-based semantic clustering and summarization approach for biomedical literature and a new summarization evaluation method publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-8-S9-S4 contributor: fullname: Yoo – volume: 15 start-page: 32 year: 2008 ident: 2023012512015777500_B7 article-title: Five-way smoking status classification using text hot-spot identification and error-correcting output codes publication-title: J. Am. Med. Inform. Assoc. doi: 10.1197/jamia.M2434 contributor: fullname: Cohen – start-page: 97 volume-title: Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing, BioNLP '09 year: 2009 ident: 2023012512015777500_B14 article-title: Towards automatic generation of gene summary contributor: fullname: Jin – volume-title: Proceedings of the Document Understanding Conference. year: 2001 ident: 2023012512015777500_B25 article-title: Experiments in single and multidocument summarization using MEAD contributor: fullname: Radev |
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