ArticleBMC bioinformatics2009
Is searching full text more effective than searching abstracts?
Article in BMC bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed.
- Sometimes the apple does fall far from the tree: a case study on automatic indexing precision errors in PubMed.Journal of the Medical Library Association : JMLA · 2025Article
- ScRAPdb: an integrated pan-omics database for the Saccharomyces cerevisiae reference assembly panel.Nucleic acids research · 2025Article
- Unlocking the potential of PubMed Central supplementary data files.Bioinformatics advances · 2025Article
- Unsupervised learning and natural language processing highlight research trends in a superbug.Frontiers in artificial intelligence · 2024Article
- Predicting substantive biomedical citations without full text.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Towards a unified search: Improving PubMed retrieval with full text.Journal of biomedical informatics · 2022Article
- GeneCup: mining PubMed and GWAS catalog for gene-keyword relationships.G3 (Bethesda, Md.) · 2022Article
- Text mining for modeling of protein complexes enhanced by machine learning.Bioinformatics (Oxford, England) · 2021Article
- Integrating image caption information into biomedical document classification in support of biocuration.Database : the journal of biological databases and curation · 2020Article
- LitSense: making sense of biomedical literature at sentence level.Nucleic acids research · 2019Article
- PubTator central: automated concept annotation for biomedical full text articles.Nucleic acids research · 2019Article
- PaperBot: open-source web-based search and metadata organization of scientific literature.BMC bioinformatics · 2019Article
- Textpresso Central: a customizable platform for searching, text mining, viewing, and curating biomedical literature.BMC bioinformatics · 2018Article
- Retrieval of publications addressing shared decision making: an evaluation of full-text searches on medical journal websites.JMIR research protocols · 2015Article
- Publication trends of shared decision making in 15 high impact medical journals: a full-text review with bibliometric analysis.BMC medical informatics and decision making · 2014Review
- PubMedMiner: Mining and Visualizing MeSH-based Associations in PubMed.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2014Article
- Using cited references to improve the retrieval of related biomedical documents.BMC bioinformatics · 2013Article
- Biblio-MetReS: a bibliometric network reconstruction application and server.BMC bioinformatics · 2011Article
- MeSH: a window into full text for document summarization.Bioinformatics (Oxford, England) · 2011Article
- A comparison and user-based evaluation of models of textual information structure in the context of cancer risk assessment.BMC bioinformatics · 2011Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
backgroundWith the growing availability of full-text articles online, scientists and other consumers of the life sciences literature now have the ability to go beyond searching bibliographic records (title, abstract, metadata) to directly access full-text content. Motivated by this emerging trend, I posed the following question: is searching full text more effective than searching abstracts? This question is answered by comparing text retrieval algorithms on MEDLINE abstracts, full-text articles, and spans (paragraphs) within full-text articles using data from the TREC 2007 genomics track evaluation. Two retrieval models are examined: bm25 and the ranking algorithm implemented in the open-source Lucene search engine.
resultsExperiments show that treating an entire article as an indexing unit does not consistently yield higher effectiveness compared to abstract-only search. However, retrieval based on spans, or paragraphs-sized segments of full-text articles, consistently outperforms abstract-only search. Results suggest that highest overall effectiveness may be achieved by combining evidence from spans and full articles.
conclusionUsers searching full text are more likely to find relevant articles than searching only abstracts. This finding affirms the value of full text collections for text retrieval and provides a starting point for future work in exploring algorithms that take advantage of rapidly-growing digital archives. Experimental results also highlight the need to develop distributed text retrieval algorithms, since full-text articles are significantly longer than abstracts and may require the computational resources of multiple machines in a cluster. The MapReduce programming model provides a convenient framework for organizing such computations.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.