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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
Published on: February 23, 2019
MeSH indexing based on automatically generated summaries
Antonio J Jimeno-Yepes1, Laura Plaza, James G Mork
1National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA. antonio.jimeno@gmail.com
BMC Bioinformatics
|June 28, 2013
Summary
Automatic summaries improve Medical Subject Headings (MeSH) indexing by enhancing precision over full-text articles. This approach aids manual indexers, potentially speeding up the process while maintaining indexing quality for MEDLINE citations.
Area of Science:
- Biomedical Informatics
- Natural Language Processing
- Information Retrieval
Background:
- Manual indexing of MEDLINE citations using Medical Subject Headings (MeSH) is time-consuming.
- The Medical Text Indexer (MTI) tool assists indexers but currently uses limited input (citations, title, abstract).
- Using full text as MTI input improves recall but reduces precision.
Purpose of the Study:
- To evaluate the effectiveness of automatically generated summaries as input for MTI.
- To determine if summaries can enhance MeSH indexing accuracy and efficiency for manual indexers.
Main Methods:
- Generated article summaries using two summarization techniques.
- Evaluated MTI indexing performance on these summaries using various algorithms.
- Compared results against full-text articles and MEDLINE citations.
Main Results:
- Automatically generated summaries yielded higher precision than full-text articles.
- Summaries achieved recall comparable to full-text articles.
- Compared to MEDLINE citations, summaries showed higher recall but lower precision.
Conclusions:
- Automatic summaries offer improved precision for MeSH indexing compared to full-text articles.
- Summarization techniques effectively capture key article content, enhancing MTI recommendations.
- Tailoring summarization to specific MeSH headings may further optimize indexing performance.
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