MeSH indexing based on automatically generated summaries.
MeSH indexing based on automatically generated summaries.
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DOI：
10.1186/1471-2105-14-208
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发表时间：
2013-06-26
影响因子：
3
通讯作者：
Díaz A
中科院分区：
文献类型：
--
作者：
Jimeno-Yepes AJ;Plaza L;Mork JG;Aronson AR;Díaz A
MEDLINE引文在美国国家医学图书馆（NLM）使用医学主题标题（MeSH）控制词汇作为参考进行手动索引。对于这项任务，人工索引员阅读文章的全文。由于MEDLINE的发展，NLM索引计划探索了能够支持索引员任务的索引方法。医学文本索引器（MTI）是由NLM索引倡议开发的一种工具，用于向索引者提供MeSH索引建议。目前，MTI的输入仅为MEDLINE引文、标题和摘要。先前的研究表明，使用全文作为MTI输入提高了召回率，但大大降低了准确率。我们建议使用从全文自动生成的摘要作为MTI的输入，用于向索引器建议MeSH标题的任务。摘要从全文中提炼出最重要的信息，这可能会增加基于MEDLINE的自动索引方法的覆盖率。我们假设，如果结果足够好，人工索引者可能会使用自动摘要代替全文，以及MTI的建议，以加快过程，同时保持高质量的索引结果。我们使用两种不同的摘要器生成了不同长度的摘要，并使用不同的算法（MTI、单个MTI组件和机器学习）评估了摘要上的MTI索引。将结果与全文文章和MEDLINE引文的结果进行比较。我们的结果表明，与全文文章相比，自动生成的摘要实现了相似的召回，但精度更高。与MEDLINE引文相比，摘要的查全率更高，但查准率较低。我们的结果表明，自动摘要比全文文章产生更好的索引。摘要可以产生与全文相似的召回，但精度更高，这似乎表明自动摘要可以有效地捕获原始文章中最重要的内容。MEDLINE引文和自动生成摘要的结合可以改进MTI提出的推荐。另一方面，索引性能可能取决于被索引的MeSH标题。因此，摘要技术可以被视为一种特征选择算法，可能必须为每个MeSH标题单独调整。
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.