SynthesisJournal of biomedical informatics2014
Text summarization in the biomedical domain: a systematic review of recent research.
Synthesis in Journal of biomedical informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 5 of them syntheses that pooled it.
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
56 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- A systematic review of automatic text summarization for biomedical literature and EHRs.Journal of the American Medical Informatics Association : JAMIA · 2021Pooled it
- Technological progress in electronic health record system optimization: Systematic review of systematic literature reviews.International journal of medical informatics · 2021Pooled it
- Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review.BMJ health & care informatics · 2021Pooled it
- A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data.International journal of medical informatics · 2019Pooled it
- Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2019Pooled it
- Increasing Women's Knowledge about HPV Using BERT Text Summarization: An Online Randomized Study.International journal of environmental research and public health · 2022Trial
- Multimodal integrated knowledge transfer to large language models through preference optimization with biomedical applications.Patterns (New York, N.Y.) · 2026Article
- Grounding large language models in clinical diagnostics.Nature communications · 2026Article
- Improving Large Language Models' Summarization Accuracy by Adding Highlights to Discharge Notes: Comparative Evaluation.JMIR medical informatics · 2025Article
- Unstructured Electronic Health Records of Dysphagic Patients Analyzed by Large Language Models.IEEE journal of translational engineering in health and medicine · 2025Article
- Ascle-A Python Natural Language Processing Toolkit for Medical Text Generation: Development and Evaluation Study.Journal of medical Internet research · 2024Article
- Leveraging artificial intelligence to summarize abstracts in lay language for increasing research accessibility and transparency.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- The power of graphs in medicine: Introducing BioGraphSum for effective text summarization.Heliyon · 2024Article
- Biomedical semantic text summarizer.BMC bioinformatics · 2024Article
- AI-Assisted Summarization of Radiologic Reports: Evaluating GPT3davinci, BARTcnn, LongT5booksum, LEDbooksum, LEDlegal, and LEDclinical.AJNR. American journal of neuroradiology · 2024Article
- Retrieval augmentation of large language models for lay language generation.Journal of biomedical informatics · 2024Article
- Information Capsule: A New Approach for Summarizing Medical Information.International journal of preventive medicine · 2024Article
- Article
- Patient Information Summarization in Clinical Settings: Scoping Review.JMIR medical informatics · 2023Article
- Initial development of tools to identify child abuse and neglect in pediatric primary care.BMC medical informatics and decision making · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
objectiveThe amount of information for clinicians and clinical researchers is growing exponentially. Text summarization reduces information as an attempt to enable users to find and understand relevant source texts more quickly and effortlessly. In recent years, substantial research has been conducted to develop and evaluate various summarization techniques in the biomedical domain. The goal of this study was to systematically review recent published research on summarization of textual documents in the biomedical domain. MATERIALS AND
methodsMEDLINE (2000 to October 2013), IEEE Digital Library, and the ACM digital library were searched. Investigators independently screened and abstracted studies that examined text summarization techniques in the biomedical domain. Information is derived from selected articles on five dimensions: input, purpose, output, method and evaluation.
resultsOf 10,786 studies retrieved, 34 (0.3%) met the inclusion criteria. Natural language processing (17; 50%) and a hybrid technique comprising of statistical, Natural language processing and machine learning (15; 44%) were the most common summarization approaches. Most studies (28; 82%) conducted an intrinsic evaluation. DISCUSSION: This is the first systematic review of text summarization in the biomedical domain. The study identified research gaps and provides recommendations for guiding future research on biomedical text summarization.
conclusionRecent research has focused on a hybrid technique comprising statistical, language processing and machine learning techniques. Further research is needed on the application and evaluation of text summarization in real research or patient care settings.
Indexed as
Identifiers
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.