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Open Access Highly Accessed Research article

MeSH indexing based on automatically generated summaries

Antonio J Jimeno-Yepes12*, Laura Plaza3, James G Mork1, Alan R Aronson1 and Alberto Díaz4

Author Affiliations

1 National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA

2 National ICT Australia, Victoria Research Laboratory, Melbourne, Australia

3 UNED NLP & IR Group, C/ Juan del Rosal 16, Madrid 28040, Spain

4 UCM NIL Group, C/Profesor José García Santesmases s/n, Madrid 28040, Spain

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BMC Bioinformatics 2013, 14:208  doi:10.1186/1471-2105-14-208

Published: 26 June 2013

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.