MeSH indexing based on automatically generated summaries
Background MEDLINE citations are manually indexed at the U.S. National Library of Medicine (NLM) using as reference the Medical Subject Headings (MeSH) controlled vocabulary. For this task, the human indexers read the full text of the article. Due to the growth of MEDLINE, the NLM Indexing Initiativ...
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| Vydané v: | BMC bioinformatics Ročník 14; číslo 1; s. 208 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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London
BioMed Central
26.06.2013
BioMed Central Ltd |
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| ISSN: | 1471-2105, 1471-2105 |
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| Abstract | Background
MEDLINE citations are manually indexed at the U.S. National Library of Medicine (NLM) using as reference the Medical Subject Headings (MeSH) controlled vocabulary. For this task, the human indexers read the full text of the article. Due to the growth of MEDLINE, the NLM Indexing Initiative explores indexing methodologies that can support the task of the indexers. Medical Text Indexer (MTI) is a tool developed by the NLM Indexing Initiative to provide MeSH indexing recommendations to indexers. Currently, the input to MTI is MEDLINE citations, title and abstract only. Previous work has shown that using full text as input to MTI increases recall, but decreases precision sharply. We propose using summaries generated automatically from the full text for the input to MTI to use in the task of suggesting MeSH headings to indexers. Summaries distill the most salient information from the full text, which might increase the coverage of automatic indexing approaches based on MEDLINE. We hypothesize that if the results were good enough, manual indexers could possibly use automatic summaries instead of the full texts, along with the recommendations of MTI, to speed up the process while maintaining high quality of indexing results.
Results
We have generated summaries of different lengths using two different summarizers, and evaluated the MTI indexing on the summaries using different algorithms: MTI, individual MTI components, and machine learning. The results are compared to those of full text articles and MEDLINE citations. Our results show that automatically generated summaries achieve similar recall but higher precision compared to full text articles. Compared to MEDLINE citations, summaries achieve higher recall but lower precision.
Conclusions
Our results show that automatic summaries produce better indexing than full text articles. Summaries produce similar recall to full text but much better precision, which seems to indicate that automatic summaries can efficiently capture the most important contents within the original articles. The combination of MEDLINE citations and automatically generated summaries could improve the recommendations suggested by MTI. On the other hand, indexing performance might be dependent on the MeSH heading being indexed. Summarization techniques could thus be considered as a feature selection algorithm that might have to be tuned individually for each MeSH heading. |
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| AbstractList | MEDLINE citations are manually indexed at the U.S. National Library of Medicine (NLM) using as reference the Medical Subject Headings (MeSH) controlled vocabulary. For this task, the human indexers read the full text of the article. Due to the growth of MEDLINE, the NLM Indexing Initiative explores indexing methodologies that can support the task of the indexers. Medical Text Indexer (MTI) is a tool developed by the NLM Indexing Initiative to provide MeSH indexing recommendations to indexers. Currently, the input to MTI is MEDLINE citations, title and abstract only. Previous work has shown that using full text as input to MTI increases recall, but decreases precision sharply. We propose using summaries generated automatically from the full text for the input to MTI to use in the task of suggesting MeSH headings to indexers. Summaries distill the most salient information from the full text, which might increase the coverage of automatic indexing approaches based on MEDLINE. We hypothesize that if the results were good enough, manual indexers could possibly use automatic summaries instead of the full texts, along with the recommendations of MTI, to speed up the process while maintaining high quality of indexing results.
We have generated summaries of different lengths using two different summarizers, and evaluated the MTI indexing on the summaries using different algorithms: MTI, individual MTI components, and machine learning. The results are compared to those of full text articles and MEDLINE citations. Our results show that automatically generated summaries achieve similar recall but higher precision compared to full text articles. Compared to MEDLINE citations, summaries achieve higher recall but lower precision.
Our results show that automatic summaries produce better indexing than full text articles. Summaries produce similar recall to full text but much better precision, which seems to indicate that automatic summaries can efficiently capture the most important contents within the original articles. The combination of MEDLINE citations and automatically generated summaries could improve the recommendations suggested by MTI. On the other hand, indexing performance might be dependent on the MeSH heading being indexed. Summarization techniques could thus be considered as a feature selection algorithm that might have to be tuned individually for each MeSH heading. Background MEDLINE citations are manually indexed at the U.S. National Library of Medicine (NLM) using as reference the Medical Subject Headings (MeSH) controlled vocabulary. For this task, the human indexers read the full text of the article. Due to the growth of MEDLINE, the NLM Indexing Initiative explores indexing methodologies that can support the task of the indexers. Medical Text Indexer (MTI) is a tool developed by the NLM Indexing Initiative to provide MeSH indexing recommendations to indexers. Currently, the input to MTI is MEDLINE citations, title and abstract only. Previous work has shown that using full text as input to MTI increases recall, but decreases precision sharply. We propose using summaries generated automatically from the full text for the input to MTI to use in the task of suggesting MeSH headings to indexers. Summaries distill the most salient information from the full text, which might increase the coverage of automatic indexing approaches based on MEDLINE. We hypothesize that if the results were good enough, manual indexers could possibly use automatic summaries instead of the full texts, along with the recommendations of MTI, to speed up the process while maintaining high quality of indexing results. Results We have generated summaries of different lengths using two different summarizers, and evaluated the MTI indexing on the summaries using different algorithms: MTI, individual MTI components, and machine learning. The results are compared to those of full text articles and MEDLINE citations. Our results show that automatically generated summaries achieve similar recall but higher precision compared to full text articles. Compared to MEDLINE citations, summaries achieve higher recall but lower precision. Conclusions Our results show that automatic summaries produce better indexing than full text articles. Summaries produce similar recall to full text but much better precision, which seems to indicate that automatic summaries can efficiently capture the most important contents within the original articles. The combination of MEDLINE citations and automatically generated summaries could improve the recommendations suggested by MTI. On the other hand, indexing performance might be dependent on the MeSH heading being indexed. Summarization techniques could thus be considered as a feature selection algorithm that might have to be tuned individually for each MeSH heading. MEDLINE citations are manually indexed at the U.S. National Library of Medicine (NLM) using as reference the Medical Subject Headings (MeSH) controlled vocabulary. For this task, the human indexers read the full text of the article. Due to the growth of MEDLINE, the NLM Indexing Initiative explores indexing methodologies that can support the task of the indexers. Medical Text Indexer (MTI) is a tool developed by the NLM Indexing Initiative to provide MeSH indexing recommendations to indexers. Currently, the input to MTI is MEDLINE citations, title and abstract only. Previous work has shown that using full text as input to MTI increases recall, but decreases precision sharply. We propose using summaries generated automatically from the full text for the input to MTI to use in the task of suggesting MeSH headings to indexers. Summaries distill the most salient information from the full text, which might increase the coverage of automatic indexing approaches based on MEDLINE. We hypothesize that if the results were good enough, manual indexers could possibly use automatic summaries instead of the full texts, along with the recommendations of MTI, to speed up the process while maintaining high quality of indexing results. We have generated summaries of different lengths using two different summarizers, and evaluated the MTI indexing on the summaries using different algorithms: MTI, individual MTI components, and machine learning. The results are compared to those of full text articles and MEDLINE citations. Our results show that automatically generated summaries achieve similar recall but higher precision compared to full text articles. Compared to MEDLINE citations, summaries achieve higher recall but lower precision. Our results show that automatic summaries produce better indexing than full text articles. Summaries produce similar recall to full text but much better precision, which seems to indicate that automatic summaries can efficiently capture the most important contents within the original articles. The combination of MEDLINE citations and automatically generated summaries could improve the recommendations suggested by MTI. On the other hand, indexing performance might be dependent on the MeSH heading being indexed. Summarization techniques could thus be considered as a feature selection algorithm that might have to be tuned individually for each MeSH heading. MEDLINE citations are manually indexed at the U.S. National Library of Medicine (NLM) using as reference the Medical Subject Headings (MeSH) controlled vocabulary. For this task, the human indexers read the full text of the article. Due to the growth of MEDLINE, the NLM Indexing Initiative explores indexing methodologies that can support the task of the indexers. Medical Text Indexer (MTI) is a tool developed by the NLM Indexing Initiative to provide MeSH indexing recommendations to indexers. Currently, the input to MTI is MEDLINE citations, title and abstract only. Previous work has shown that using full text as input to MTI increases recall, but decreases precision sharply. We propose using summaries generated automatically from the full text for the input to MTI to use in the task of suggesting MeSH headings to indexers. Summaries distill the most salient information from the full text, which might increase the coverage of automatic indexing approaches based on MEDLINE. We hypothesize that if the results were good enough, manual indexers could possibly use automatic summaries instead of the full texts, along with the recommendations of MTI, to speed up the process while maintaining high quality of indexing results.BACKGROUNDMEDLINE citations are manually indexed at the U.S. National Library of Medicine (NLM) using as reference the Medical Subject Headings (MeSH) controlled vocabulary. For this task, the human indexers read the full text of the article. Due to the growth of MEDLINE, the NLM Indexing Initiative explores indexing methodologies that can support the task of the indexers. Medical Text Indexer (MTI) is a tool developed by the NLM Indexing Initiative to provide MeSH indexing recommendations to indexers. Currently, the input to MTI is MEDLINE citations, title and abstract only. Previous work has shown that using full text as input to MTI increases recall, but decreases precision sharply. We propose using summaries generated automatically from the full text for the input to MTI to use in the task of suggesting MeSH headings to indexers. Summaries distill the most salient information from the full text, which might increase the coverage of automatic indexing approaches based on MEDLINE. We hypothesize that if the results were good enough, manual indexers could possibly use automatic summaries instead of the full texts, along with the recommendations of MTI, to speed up the process while maintaining high quality of indexing results.We have generated summaries of different lengths using two different summarizers, and evaluated the MTI indexing on the summaries using different algorithms: MTI, individual MTI components, and machine learning. The results are compared to those of full text articles and MEDLINE citations. Our results show that automatically generated summaries achieve similar recall but higher precision compared to full text articles. Compared to MEDLINE citations, summaries achieve higher recall but lower precision.RESULTSWe have generated summaries of different lengths using two different summarizers, and evaluated the MTI indexing on the summaries using different algorithms: MTI, individual MTI components, and machine learning. The results are compared to those of full text articles and MEDLINE citations. Our results show that automatically generated summaries achieve similar recall but higher precision compared to full text articles. Compared to MEDLINE citations, summaries achieve higher recall but lower precision.Our results show that automatic summaries produce better indexing than full text articles. Summaries produce similar recall to full text but much better precision, which seems to indicate that automatic summaries can efficiently capture the most important contents within the original articles. The combination of MEDLINE citations and automatically generated summaries could improve the recommendations suggested by MTI. On the other hand, indexing performance might be dependent on the MeSH heading being indexed. Summarization techniques could thus be considered as a feature selection algorithm that might have to be tuned individually for each MeSH heading.CONCLUSIONSOur results show that automatic summaries produce better indexing than full text articles. Summaries produce similar recall to full text but much better precision, which seems to indicate that automatic summaries can efficiently capture the most important contents within the original articles. The combination of MEDLINE citations and automatically generated summaries could improve the recommendations suggested by MTI. On the other hand, indexing performance might be dependent on the MeSH heading being indexed. Summarization techniques could thus be considered as a feature selection algorithm that might have to be tuned individually for each MeSH heading. Background MEDLINE citations are manually indexed at the U.S. National Library of Medicine (NLM) using as reference the Medical Subject Headings (MeSH) controlled vocabulary. For this task, the human indexers read the full text of the article. Due to the growth of MEDLINE, the NLM Indexing Initiative explores indexing methodologies that can support the task of the indexers. Medical Text Indexer (MTI) is a tool developed by the NLM Indexing Initiative to provide MeSH indexing recommendations to indexers. Currently, the input to MTI is MEDLINE citations, title and abstract only. Previous work has shown that using full text as input to MTI increases recall, but decreases precision sharply. We propose using summaries generated automatically from the full text for the input to MTI to use in the task of suggesting MeSH headings to indexers. Summaries distill the most salient information from the full text, which might increase the coverage of automatic indexing approaches based on MEDLINE. We hypothesize that if the results were good enough, manual indexers could possibly use automatic summaries instead of the full texts, along with the recommendations of MTI, to speed up the process while maintaining high quality of indexing results. Results We have generated summaries of different lengths using two different summarizers, and evaluated the MTI indexing on the summaries using different algorithms: MTI, individual MTI components, and machine learning. The results are compared to those of full text articles and MEDLINE citations. Our results show that automatically generated summaries achieve similar recall but higher precision compared to full text articles. Compared to MEDLINE citations, summaries achieve higher recall but lower precision. Conclusions Our results show that automatic summaries produce better indexing than full text articles. Summaries produce similar recall to full text but much better precision, which seems to indicate that automatic summaries can efficiently capture the most important contents within the original articles. The combination of MEDLINE citations and automatically generated summaries could improve the recommendations suggested by MTI. On the other hand, indexing performance might be dependent on the MeSH heading being indexed. Summarization techniques could thus be considered as a feature selection algorithm that might have to be tuned individually for each MeSH heading. |
| ArticleNumber | 208 |
| Audience | Academic |
| Author | Jimeno-Yepes, Antonio J Aronson, Alan R Díaz, Alberto Mork, James G Plaza, Laura |
| AuthorAffiliation | 1 National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA 4 UCM NIL Group, C/Profesor José García Santesmases s/n, Madrid 28040, Spain 2 National ICT Australia, Victoria Research Laboratory, Melbourne, Australia 3 UNED NLP & IR Group, C/ Juan del Rosal 16, Madrid 28040, Spain |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/23802936$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/j.jbi.2003.11.003 10.1145/1008992.1009035 10.1145/502585.502647 10.1093/bioinformatics/bti783 10.1186/1471-2105-9-108 10.1145/1656274.1656278 10.1016/0306-4573(95)00052-I 10.1613/jair.1523 10.1145/321510.321519 10.1093/bioinformatics/btp249 10.1186/1471-2105-8-423 10.5626/JCSE.2012.6.2.151 10.1016/j.artmed.2004.07.017 10.1186/1471-2105-8-S9-S4 10.1197/jamia.M1641 10.3115/1596431.1596442 10.1147/rd.22.0159 10.1016/j.artmed.2011.06.005 10.1016/S0169-7552(98)00110-X 10.1145/2110363.2110450 10.1075/nlp.3 10.1186/1471-2105-11-569 10.1016/j.ipm.2007.01.026 10.1136/jamia.2009.002733 |
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| References | C Gay (5967_CR5) 2005 Ruch P (5967_CR6) 2006; 22 A Jimeno-Yepes (5967_CR48) 2012; 6 D Shen (5967_CR46) 2004 A Aronson (5967_CR3) 2000 G Poulter (5967_CR7) 2008; 9 H Edmundson (5967_CR17) 1969; 2 L Plaza (5967_CR23) 2011; 53 E Lloret (5967_CR30) 2010 R Brandow (5967_CR15) 1995; 5 5967_CR27 R Mihalcea (5967_CR19) 2004 Mani I (5967_CR13) 2001 M Fiszman (5967_CR25) 2004 M Yetisgen-Yildiz (5967_CR10) 2005 Y Aphinyanaphongs (5967_CR9) 2005; 12 A Névéol (5967_CR36) 2007 A Jimeno-Yepes (5967_CR33) 2011 5967_CR35 5967_CR34 ME Funk (5967_CR45) 1983; 71 5967_CR39 5967_CR38 5967_CR37 L Plaza (5967_CR40) 2011 G Erkan (5967_CR18) 2004; 22 Z Shi (5967_CR24) 2007 5967_CR42 H Luhn (5967_CR16) 1958; 2 D Shen (5967_CR28) 2004 S Brin (5967_CR41) 1998; 30 M Hall (5967_CR47) 2009; 11 J Lin (5967_CR11) 2007; 8 KW Fung (5967_CR32) 2005 5967_CR49 A Kolcz (5967_CR51) 2001 CY Lin (5967_CR44) 2004 A Kolcz (5967_CR29) 2001 L Reeve (5967_CR21) 2007; 43 A Jimeno Yepes (5967_CR50) 2012 A Aronson (5967_CR31) 2010; 17 I Yoo (5967_CR22) 2007; 8 5967_CR1 5967_CR2 CY Lin (5967_CR43) 2004 D Trieschnigg (5967_CR12) 2009; 25 A Aronson (5967_CR4) 2004 S Afantenos (5967_CR14) 2005; 33 S Fleischman (5967_CR20) 2008 T Rindflesch (5967_CR26) 2003; 36 A Kastrin (5967_CR8) 2009; 48 |
| References_xml | – ident: 5967_CR34 – volume: 71 start-page: 176 issue: 2 year: 1983 ident: 5967_CR45 publication-title: Bull Med Libr Assoc – volume: 36 start-page: 462 year: 2003 ident: 5967_CR26 publication-title: J Biomed Inform doi: 10.1016/j.jbi.2003.11.003 – ident: 5967_CR38 – start-page: 242 volume-title: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval year: 2004 ident: 5967_CR46 doi: 10.1145/1008992.1009035 – start-page: 365 volume-title: Proceedings of the Tenth International Conference on Information and Knowledge Management year: 2001 ident: 5967_CR29 doi: 10.1145/502585.502647 – volume: 22 start-page: 658 issue: 6 year: 2006 ident: 5967_CR6 publication-title: Bioinformatics doi: 10.1093/bioinformatics/bti783 – volume: 9 start-page: 108 year: 2008 ident: 5967_CR7 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-9-108 – volume: 11 start-page: 10 year: 2009 ident: 5967_CR47 publication-title: ACM SIGKDD Explorations Newsl doi: 10.1145/1656274.1656278 – start-page: 849 volume-title: AMIA Annual Symposium Proceedings Volume 2005 year: 2005 ident: 5967_CR10 – start-page: 553 volume-title: AMIA Annual Symposium Proceedings, Volume 2007 year: 2007 ident: 5967_CR36 – volume: 5 start-page: 675 issue: 31 year: 1995 ident: 5967_CR15 publication-title: Inf Proc Manage doi: 10.1016/0306-4573(95)00052-I – volume: 22 start-page: 457 year: 2004 ident: 5967_CR18 publication-title: J Artif Intell Res(JAIR) doi: 10.1613/jair.1523 – ident: 5967_CR27 – volume: 2 start-page: 264 issue: 16 year: 1969 ident: 5967_CR17 publication-title: J Assoc Comput Mach doi: 10.1145/321510.321519 – start-page: 242 volume-title: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’04) year: 2004 ident: 5967_CR28 – start-page: 266 volume-title: AMIA Annual Symposium Proceedings, Volume 2005 year: 2005 ident: 5967_CR32 – ident: 5967_CR37 – start-page: 17 volume-title: Proceedings of the AMIA Symposium year: 2000 ident: 5967_CR3 – volume: 25 start-page: 1412 issue: 11 year: 2009 ident: 5967_CR12 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btp249 – start-page: 268 volume-title: Medinfo 2004: proceedings of the 11th World Conference on Medical Informatics year: 2004 ident: 5967_CR4 – volume: 8 start-page: 423 year: 2007 ident: 5967_CR11 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-8-423 – start-page: (255) volume-title: BMC Bioinformatics year: 2011 ident: 5967_CR40 – start-page: 74 volume-title: Proceedings of the ACL 2004 Workshop: Text Summarization Branches Out year: 2004 ident: 5967_CR43 – volume: 6 start-page: 151 issue: 2 year: 2012 ident: 5967_CR48 publication-title: J Comput Sci Eng doi: 10.5626/JCSE.2012.6.2.151 – start-page: 365 volume-title: Proceedings of the Tenth International Conference on Information and Knowledge Management year: 2001 ident: 5967_CR51 doi: 10.1145/502585.502647 – volume: 33 start-page: 157 issue: 2 year: 2005 ident: 5967_CR14 publication-title: Artif Intell Med doi: 10.1016/j.artmed.2004.07.017 – volume-title: Proceedings of the 4th NTCIR Workshop on Research in Information Access Technologies Information Retrieval, Question Answering and Summarization year: 2004 ident: 5967_CR44 – ident: 5967_CR1 – start-page: 271 volume-title: AMIA Annual Symposium Proceedings Volume 2005 year: 2005 ident: 5967_CR5 – volume: 48 start-page: 10 year: 2009 ident: 5967_CR8 publication-title: Methods Inf Med – volume: 8 start-page: S4 issue: 9 year: 2007 ident: 5967_CR22 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-8-S9-S4 – ident: 5967_CR42 – volume: 12 start-page: 207 issue: 2 year: 2005 ident: 5967_CR9 publication-title: J Am Med Inform Assoc doi: 10.1197/jamia.M1641 – start-page: 284 volume-title: Proceedings of the Canadian Conference on Artificial Intelligence year: 2007 ident: 5967_CR24 – start-page: 76 volume-title: Proceedings of the HLT-NAACL Workshop on Computational Lexical Semantics year: 2004 ident: 5967_CR25 doi: 10.3115/1596431.1596442 – volume-title: Proceedings of the Fourth International Symposium on Languages in Biology and Medicine year: 2011 ident: 5967_CR33 – start-page: 107 volume-title: Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text year: 2010 ident: 5967_CR30 – ident: 5967_CR35 – volume-title: Language and Medicine year: 2008 ident: 5967_CR20 – ident: 5967_CR2 – volume: 2 start-page: 1159 issue: 2 year: 1958 ident: 5967_CR16 publication-title: IBM J Res Dev doi: 10.1147/rd.22.0159 – volume: 53 start-page: 1 year: 2011 ident: 5967_CR23 publication-title: Artif Intell Med doi: 10.1016/j.artmed.2011.06.005 – volume: 30 start-page: 1 year: 1998 ident: 5967_CR41 publication-title: Comput Netw ISDN Syst doi: 10.1016/S0169-7552(98)00110-X – start-page: 737 volume-title: Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium year: 2012 ident: 5967_CR50 doi: 10.1145/2110363.2110450 – volume-title: Automatic Summarization year: 2001 ident: 5967_CR13 doi: 10.1075/nlp.3 – ident: 5967_CR39 doi: 10.1186/1471-2105-11-569 – ident: 5967_CR49 – volume: 43 start-page: 1765 year: 2007 ident: 5967_CR21 publication-title: Inf Proc Manage doi: 10.1016/j.ipm.2007.01.026 – start-page: 404 volume-title: Proceedings of the Conference EMNLP 2004 year: 2004 ident: 5967_CR19 – volume: 17 start-page: 229 issue: 3 year: 2010 ident: 5967_CR31 publication-title: J Am Med Inform Assoc doi: 10.1136/jamia.2009.002733 |
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| Title | MeSH indexing based on automatically generated summaries |
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