ArticleJournal of biomedical informatics2016
Extractive text summarization system to aid data extraction from full text in systematic review development.
Article in Journal of biomedical informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.
What it found
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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.
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Who cites it
17 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Double burden of malnutrition among households in Ethiopia: a systematic review and meta-analysis.Frontiers in public health · 2024Pooled it
- A systematic review of automatic text summarization for biomedical literature and EHRs.Journal of the American Medical Informatics Association : JAMIA · 2021Pooled it
- Data extraction methods for systematic review (semi)automation: Update of a living systematic review.F1000Research · 2021Pooled it
- Effect of Nutrition Interventions on the Dietary Diversity Status Among Pregnant Women in Ethiopia: Systematic Review and Meta-Analysis.Food science & nutrition · 2025Review
- Efficient evidence selection for systematic reviews in traditional Chinese medicine.BMC medical research methodology · 2025Article
- Enhanced transformer for length-controlled abstractive summarization based on summary output area.PeerJ. Computer science · 2025Article
- Leveraging artificial intelligence to summarize abstracts in lay language for increasing research accessibility and transparency.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Text summarization for pharmaceutical sciences using hierarchical clustering with a weighted evaluation methodology.Scientific reports · 2024Article
- Automation of systematic reviews of biomedical literature: a scoping review of studies indexed in PubMed.Systematic reviews · 2024Article
- Retrieval augmentation of large language models for lay language generation.Journal of biomedical informatics · 2024Article
- A Scoping Review of Adopted Information Extraction Methods for RCTs.Medical journal of the Islamic Republic of Iran · 2023Article
- A novel centroid based sentence classification approach for extractive summarization of COVID-19 news reports.International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management · 2023Article
- Applications of natural language processing in ophthalmology: present and future.Frontiers in medicine · 2022Review
- Clinical Context-Aware Biomedical Text Summarization Using Deep Neural Network: Model Development and Validation.Journal of medical Internet research · 2020Article
- A tutorial on methodological studies: the what, when, how and why.BMC medical research methodology · 2020Review
- Impact of Automatic Query Generation and Quality Recognition Using Deep Learning to Curate Evidence From Biomedical Literature: Empirical Study.JMIR medical informatics · 2019Article
- Improving reference prioritisation with PICO recognition.BMC medical informatics and decision making · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
objectivesExtracting data from publication reports is a standard process in systematic review (SR) development. However, the data extraction process still relies too much on manual effort which is slow, costly, and subject to human error. In this study, we developed a text summarization system aimed at enhancing productivity and reducing errors in the traditional data extraction process.
methodsWe developed a computer system that used machine learning and natural language processing approaches to automatically generate summaries of full-text scientific publications. The summaries at the sentence and fragment levels were evaluated in finding common clinical SR data elements such as sample size, group size, and PICO values. We compared the computer-generated summaries with human written summaries (title and abstract) in terms of the presence of necessary information for the data extraction as presented in the Cochrane review's study characteristics tables.
resultsAt the sentence level, the computer-generated summaries covered more information than humans do for systematic reviews (recall 91.2% vs. 83.8%, p<0.001). They also had a better density of relevant sentences (precision 59% vs. 39%, p<0.001). At the fragment level, the ensemble approach combining rule-based, concept mapping, and dictionary-based methods performed better than individual methods alone, achieving an 84.7% F-measure.
conclusionComputer-generated summaries are potential alternative information sources for data extraction in systematic review development. Machine learning and natural language processing are promising approaches to the development of such an extractive summarization system.
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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.