Automatic Text Summarization of Biomedical Text Data: A Systematic Review
Open accessAbstract
In recent years, the evolution of technology has led to an increase in text data obtained from many sources. In the biomedical domain, text information has also evidenced this accelerated growth, and automatic text summarization systems play an essential role in optimizing physicians’ time resources and identifying relevant information. In this paper, we present a systematic review in recent research of text summarization for biomedical textual data, focusing mainly on the methods employed, type of input data text, areas of application, and evaluation metrics used to assess systems. The survey was limited to the period between 1st January 2014 and 15th March 2022. The data collected was obtained from WoS, IEEE, and ACM digital libraries, while the search strategies were developed with the help of experts in NLP techniques and previous systematic reviews. The four phases of a systematic review by PRISMA methodology were conducted, and five summarization factors were determined to assess the studies included: Input, Purpose, Output, Method, and Evaluation metric. Results showed that 3.5% of 801 studies met the inclusion criteria. Moreover, Single-document, Biomedical Literature, Generic, and Extractive summarization proved to be the most common approaches employed, while techniques based on Machine Learning were performed in 16 studies and Rouge (Recall-Oriented Understudy for Gisting Evaluation) was reported as the evaluation metric in 26 studies. This review found that in recent years, more transformer-based methodologies for summarization purposes have been implemented compared to a previous survey. Additionally, there are still some challenges in text summarization in different domains, especially in the biomedical field in terms of demand for further research.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Most authors focused on summarization of radiology reports, i.e., on the creation of a shorter version that includes all important aspects at the same level of text complexity.
“So far, most authors focused on summarization of radiology reports, i.e., on the creation of a shorter version that includes all important aspects at the same level of text complexity [23–26].”
From ChatGPT makes medicine easy to swallow: an exploratory case study on simplified radiology reports (2023) · cites this paper as [26]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works
Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P. A., Clarke, M., Devereaux, P. J., Kleijnen, J., & Moher, D. (2009) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2009.06.006 · Cited by 10,578 · see citing works
(2004) · Cited by 9,718 · see citing works
BERTScore: Evaluating Text Generation with BERT
Zhang, T., Kishore, V., Wu, F., Weinberger, K. Q., & Artzi, Y. (2019) · arXiv · doi:10.48550/arxiv.1904.09675 · Cited by 2,865 · see citing works
Benefits and drawbacks of electronic health record systems
Menachemi, N., & Collum (2011) · Risk Management and Healthcare Policy · doi:10.2147/rmhp.s12985 · Cited by 909 · see citing works
Aggarwal, C. C., & Zhai, C. (2012) · Springer eBooks · doi:10.1007/978-1-4614-3223-4 · Cited by 853 · see citing works
Mani, I. (2001) · Natural language processing · doi:10.1075/nlp.3 · Cited by 841 · see citing works
Recent automatic text summarization techniques: a survey
Gambhir, M., & Gupta, V. (2016) · Artificial Intelligence Review · doi:10.1007/s10462-016-9475-9 · Cited by 729 · see citing works
Tsatsaronis, G., Balikas, G., Malakasiotis, P., Partalas, I., Zschunke, M., Alvers, M. R., Weissenborn, D., Krithara, A., Petridis, S., Polychronopoulos, D., et al. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0564-6 · Cited by 598 · see citing works
A Survey of Text Summarization Techniques
Nenkova, A., & McKeown, K. R. (2012) · Springer eBooks · doi:10.1007/978-1-4614-3223-4_3 · Cited by 592 · see citing works
Text Summarization Techniques: A Brief Survey
Allahyari, M., Pouriyeh, S., Assefi, M., Safaei, S., Trippe, E. D., Gutiérrez, J. B., & Kochut, K. J. (2017) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2017.081052 · Cited by 581 · see citing works
Concept annotation in the CRAFT corpus
Bada, M. A., Eckert, M., Evans, D., Garcia, K., Shipley, K., Sitnikov, D. M., Baumgartner, W. A., Cohen, K. B., Verspoor, K. M., Blake, J. A., et al. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-161 · Cited by 276 · see citing works
Review of automatic text summarization techniques & methods
Widyassari, A. P., Rustad, S., Shidik, G. F., et al. (2020) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2020.05.006 · Cited by 241 · see citing works
Text mining of cancer-related information: Review of current status and future directions
Spasić, ., Livsey, J. E., Keane, J. A., & Nenadić, G. (2014) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2014.06.009 · Cited by 208 · see citing works
Text summarization in the biomedical domain: A systematic review of recent research
Mishra, R., Bian, J., Marcelo Fiszman, B., Weir, C. R., Jonnalagadda, S., Mostafa, J., & Fiol, G. D. (2014) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2014.06.009 · Cited by 201 · see citing works
Applying regression models to query-focused multi-document summarization
Ouyang, Y., Li, W., Li, S., & Lu, Q. (2010) · Information Processing & Management · doi:10.1016/j.ipm.2010.03.005 · Cited by 165 · see citing works
Summarization from medical documents: a survey
Afantenos, S., Karkaletsis, V., & Stamatopoulos, P. (2004) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2004.07.017 · Cited by 164 · see citing works
Evaluation Measures for Text Summarization
Steinberger, J., & Ježek, K. (2012) · Cited by 145 · see citing works
Multi-document abstractive summarization using ILP based multi-sentence compressionPreprint
Banerjee, S., Mitra, P., & Sugiyama, K. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.07034 · Cited by 129 · see citing works
Enhancing diversity, coverage and balance for summarization through structure learning
Li, L., Zhou, K., Xue, G.-R., Hongyuan Zha, B., & Yu, Y. H. (2009) · International World Wide Web Conference (WWW) · doi:10.1145/1526709.1526720 · Cited by 119 · see citing works
A Systematic Review of Natural Language Processing in Healthcare
Iroju, O. G., & Janet, O. (2015) · International Journal of Information Technology and Computer Science · doi:10.5815/ijitcs.2015.08.07 · Cited by 111 · see citing works
MeanSum: A Neural Model for Unsupervised Multi-document Abstractive SummarizationPreprint
Chu, E., & Liu, P. J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.05739 · Cited by 109 · see citing works
Deep Learning Techniques for Biomedical and Health Informatics
(2019) · Studies in big data · doi:10.1007/978-3-030-33966-1 · Cited by 104 · see citing works
Automatic summarising: factors and directionsPreprint
Jones, K. S. (1998) · arXiv (Cornell University) · doi:10.48550/arxiv.cmp-lg/9805011 · Cited by 103 · see citing works
NLP based Machine Learning Approaches for Text Summarization
Rahul, Adhikari, S., & Monika (2020) · International Conference on Computing Methodologies and Communication (ICCMC) · doi:10.1109/iccmc48092.2020.iccmc-00099 · Cited by 92 · see citing works
Question-driven summarization of answers to consumer health questions
Savery, M. E., Abacha, A. B., Gayen, S., & Demner‐Fushman, D. (2020) · Scientific Data · doi:10.1038/s41597-020-00667-z · Cited by 88 · see citing works
Moradi, M., Dorffner, G., & Samwald, M. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.105117 · Cited by 85 · see citing works
Moradi, M., & Ghadiri, N. (2017) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2017.11.004 · Cited by 79 · see citing works
Strategies for coping with information overload
Smith, R. W. (2010) · BMJ · doi:10.1136/bmj.c7126 · Cited by 75 · see citing works
Automatic prediction of coronary artery disease from clinical narratives
Buchan, K., Filannino, M., & Uzuner, Ö. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.06.019 · Cited by 75 · see citing works
Ontology-Aware Clinical Abstractive Summarization
MacAvaney, S., Sotudeh, S., Cohan, A., Goharian, N., Talati, I. A., & Filice, R. W. (2019) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3331184.3331319 · Cited by 74 · see citing works
Comparative Study of Text Summarization Methods
Munot, N., & Govilkar, S. S. (2014) · International Journal of Computer Applications · doi:10.5120/17870-8810 · Cited by 73 · see citing works
Unsupervised and self-supervised deep learning approaches for biomedical text mining
Nadif, M., & Role, F. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab016 · Cited by 73 · see citing works
Bui, D. D. A., Del Fiol, G., Hurdle, J. F., & Jonnalagadda, S. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.10.014 · Cited by 70 · see citing works
Biomedical-domain pre-trained language model for extractive summarization
Du, Y., Li, Q., Wang, L., & He, Y.-Q. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105964 · Cited by 65 · see citing works
A systematic review of automatic text summarization for biomedical literature and EHRs
Wang, M., Wang, M., Yu, F., Yang, Y., Walker, J. S., & Mostafa, J. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab143 · Cited by 63 · see citing works
Identifying Patients with Depression Using Free-text Clinical Documents
Zhou, L., Baughman, A. W., Lei, V. J., Lai, K. H., Navathe, A. S., CHANG, F. Y., Margarita, S., Topaz, M., Zhong, F., Murrali, M., et al. (2015) · Studies in health technology and informatics · doi:10.3233/978-1-61499-564-7-629 · Cited by 54 · see citing works
Topaz, M., Lai, K. H., Dowding, D., Lei, V. J., Zisberg, A., Bowles, K. H., & Zhou, L. (2016) · International Journal of Nursing Studies · doi:10.1016/j.ijnurstu.2016.09.013 · Cited by 49 · see citing works
The electronic health record as a clinical trials tool: Opportunities and challenges
Bertagnolli, M. M., Anderson, B., Quina, A., & Piantadosi, S. (2020) · Clinical Trials · doi:10.1177/1740774520913819 · Cited by 48 · see citing works
CIBS: A biomedical text summarizer using topic-based sentence clustering
Moradi, M. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.11.006 · Cited by 45 · see citing works
Combine clustering and frequent itemsets mining to enhance biomedical text summarization
Rouane, O., Belhadef, H., & Bouakkaz, M. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.06.002 · Cited by 45 · see citing works
Abstractive Multi-document Summarization with Semantic Information Extraction
Li, W. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1219 · Cited by 43 · see citing works
SparkText: Biomedical Text Mining on Big Data Framework
Ye, Z., Tafti, A. P., He, K., Wang, K., & He, M. M. (2016) · PLoS ONE · doi:10.1371/journal.pone.0162721 · Cited by 42 · see citing works
Extractive summarization of clinical trial descriptions
Gulden, C., Kirchner, M., Schüttler, C., Hinderer, M., Kampf, M., Prokosch, H., & Toddenroth, D. (2019) · Int. J. Medical Informatics · doi:10.1016/j.ijmedinf.2019.05.019 · Cited by 41 · see citing works
Summarization of biomedical articles using domain-specific word embeddings and graph ranking
Moradi, M., Dashti, M., & Samwald, M. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103452 · Cited by 41 · see citing works
Quantifying the informativeness for biomedical literature summarization: An itemset mining method
Moradi, M., & Ghadiri, N. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2017.05.011 · Cited by 40 · see citing works
Review on text summarization evaluation methods
Indu, M., & Kavitha, K. V. (2016) · International Conference on Research Advances in Integrated Navigation Systems (RAINS) · doi:10.1109/rains.2016.7764406 · Cited by 35 · see citing works
Data Augmentation for Abstractive Query-Focused Multi-Document Summarization
Pasunuru, R., Çelikyılmaz, A., Galley, M., Xiong, C., Zhang, Y., Bansal, M., & Gao, J. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i15.17611 · Cited by 34 · see citing works
Chen, Y.-P., Chen, Y.-P., Lin, J.-J., Huang, C., & Lai, F. (2020) · JMIR Medical Informatics · doi:10.2196/17787 · Cited by 33 · see citing works
Automatic text summarization and it's methods - a review
Bhatia, N., & Jaiswal, A. (2016) · doi:10.1109/confluence.2016.7508049 · Cited by 28 · see citing works
Delivering Clinical Evidence Where It's Needed
Davidoff, F. (2011) · JAMA · doi:10.1001/jama.2011.619 · Cited by 27 · see citing works
MultiGBS: A multi-layer graph approach to biomedical summarization
Davoodijam, E., Ghadiri, N., Shahreza, M. L., & Rinaldi, F. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103706 · Cited by 26 · see citing works
Fine-tuning the BERTSUMEXT model for Clinical Report Summarization
Vinod, P., Safar, S., Mathew, D., Venugopal, P., Joly, L. M., & George, J. (2020) · International Conference for Emerging Technology (INCET) · doi:10.1109/incet49848.2020.9154087 · Cited by 22 · see citing works
Gaur, M., Aribandi, V., Kursuncu, U., Alambo, A., Shalin, V. L., Thirunarayan, K., Beich, J., Narasimhan, M., & Sheth, A. (2021) · JMIR Mental Health · doi:10.2196/20865 · Cited by 21 · see citing works
Keyphrase extraction methodology from short abstracts of medical documents
Amer, E., & Fouad, K. M. (2016) · Cairo International Biomedical Engineering Conference · doi:10.1109/cibec.2016.7836091 · Cited by 20 · see citing works
Lee, E. K., & Uppal, K. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01330-8 · Cited by 15 · see citing works
Frequent Itemsets as Meaningful Events in Graphs for Summarizing Biomedical Texts
Moradi, M. (2018) · International Conference on Computer and Knowledge Engineering · doi:10.1109/iccke.2018.8566651 · Cited by 14 · see citing works
Extractive Text Summarization Using Ontology and Graph-Based Method
Yongkiatpanich, C., & Wichadakul, D. (2019) · IEEE International Conference on Computer and Communication Systems (ICCCS) · doi:10.1109/ccoms.2019.8821755 · Cited by 13 · see citing works
Nguyen, E., Theodorakopoulos, D., Pathak, S., Geerdink, J., Vijlbrief, O., van Keulen, M., & Seifert, C. (2020) · IEEE International Conference on Cognitive Machine Intelligence (CogMI) · doi:10.1109/cogmi50398.2020.00019 · Cited by 13 · see citing works
Domain-Aware Abstractive Text Summarization for Medical Documents
Gigioli, P., Sagar, N., Rao, A. S., & Voyles, J. (2018) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2018.8621539 · Cited by 13 · see citing works
Application of Biomedical Text Mining
Gong, L. (2018) · InTech eBooks · doi:10.5772/intechopen.75924 · Cited by 12 · see citing works
Nutri-bullets: Summarizing Health Studies by Composing Segments
Shah, D., Yu, L., Lei, T., & Barzilay, R. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i15.17624 · Cited by 7 · see citing works
Self-supervised extractive text summarization for biomedical literatures
Xie, T., Zhen, Y., Li, T., Li, C., & Ge, Y. (2021) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi52183.2021.00091 · Cited by 7 · see citing works
Purbawa, D. P., Malikhah, M., Anggraini, R. N. E., & Sarno, R. (2021) · International Conference on Information & Communication Technology and System (ICTS) · doi:10.1109/icts52701.2021.9607951 · Cited by 7 · see citing works
A Hybrid Multi-answer Summarization Model for the Biomedical Question-Answering System
Nguyen, Q.-A., Duong, Q.-H., Nguyen, M., Nguyen, H.-S., Le, H.-Q., Can, D.-C., Thanh, T. D., & Tran, M.-V. (2021) · International Conference on Knowledge and Systems Engineering/Proceedings · doi:10.1109/kse53942.2021.9648640 · Cited by 7 · see citing works
Evolutionary Algorithm based Ensemble Extractive Summarization for Developing Smart Medical System
Mallick, C., Das, A. K., Nayak, J., Pelusi, D., & Vimal, S. (2021) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-020-00412-5 · Cited by 6 · see citing works
Extractive Text Summarization for COVID-19 Medical Records
Deepika, S., Lakshmi Krishna, N., & Shridevi, S. (2021) · Innovations in Power and Advanced Computing Technologies (i-PACT) · doi:10.1109/i-pact52855.2021.9697019 · Cited by 6 · see citing works
A Concise Survey on Datasets, Tools and Methods for Biomedical Text
Johnsi, R., Kumar, G. B., & Sariki, T. P. (2022) · International Journal of Applied Engineering Research · doi:10.37622/ijaer/17.3.2022.200-217 · Cited by 6 · see citing works
Finding Clinical Knowledge from MEDLINE Abstracts by Text Summarization Technique
Sibunruang, C., & Polpinij, J. (2018) · doi:10.23919/incit.2018.8584867 · Cited by 5 · see citing works
Towards neural abstractive clinical trial text summarization with sequence to sequence models
Cintas, C., Ogallo, W., Walcott-Bryant, A., Remy, S. L., Akinwande, V., & Osebe, S. (2019) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi.2019.8904526 · Cited by 5 · see citing works
Query Specific Focused Summarization of Biomedical Journal Articles
Rai, A., Sangwan, S., Goel, T., Verma, I., & Dey, L. (2021) · Annals of Computer Science and Information Systems · doi:10.15439/2021f128 · Cited by 4 · see citing works
Exploring Multi Feature Optimization for Summarizing Clinical Trial Descriptions
Reddy, S. M., & Miriyala, S. (2020) · IEEE International Conference on Multimedia Big Data · doi:10.1109/bigmm50055.2020.00059 · Cited by 3 · see citing works
Flagged first, then by citations
Research works printed but not linked: 6
- Aggarwal, C.C., Zhai, C., Eds.; Springer: Berlin/Heidelberg, Germany, 2012; pp. 1-10. [CrossRef] · DOI
- Maybury, M. Advances in Automatic Text Summarization; MIT Press: Cambridge, MA, USA, 1999.
- Jones, K.S.; Galliers, J.R. Evaluating Natural Language Processing Systems: An Analysis and Review; Springer: Berlin/Heidelberg, Germany, 1996.
- Deaton, J. Transformers and Pointer-Generator Networks for Abstractive Summarization. 2019. Available online: https://www.semanticscholar.org/paper/Transformers-and-Pointer-Generator-Networks-for-Deaton/46adc063c1c46e0 2f6457e45503cbb65495f6494 (accessed on 29 June 2022).
- Jurafsky, D.; Martin, J.H. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition; Pearson/Prentice Hall: Hoboken, NJ, USA, 2009.
- Wang, L.L.; Lo, K.; Chandrasekhar, Y.; Reas, R.; Yang, J.; Burdick, D.; Eide, D.; Funk, K.; Katsis, Y.; Kinney, R.M.; et al. CORD-19: The COVID-19 Open Research Dataset. In Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020, Online; Association for Computational Linguistics: Stroudsburg, PA, USA, 2020.
Cited by
Sorted by citations
ChatGPT makes medicine easy to swallow: an exploratory case study on simplified radiology reports
Jeblick, K., Schachtner, B., Dexl, J., Mittermeier, A., Stüber, A. T., Topalis, J., Weber, T., Wesp, P., Sabel, B. O., Ricke, J., et al. (2023) · European Radiology · doi:10.1007/s00330-023-10213-1 · Cited by 549 · see citing works
Islam, M. S., Kabir, M. N., Ghani, N. A., Zamli, K. Z., Zulkifli, N. S. A., Rahman, M. M., & Moni, M. A. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-023-10651-9 · Cited by 170 · see citing works
Park, J., Oh, K., Han, K., & Lee, Y. H. (2024) · Scientific Reports · doi:10.1038/s41598-024-63824-z · Cited by 94 · see citing works
A Systematic Review on Text Summarization: Techniques, Challenges, Opportunities
Sharma, K. P., Yajid, M. S. A., Gowrishankar, J., Mahajan, R., Alsoud, A. R., Jadhav, A., & Singh, D. (2025) · Expert Systems · doi:10.1111/exsy.13833 · Cited by 44 · see citing works
Shi, Y., Ren, P., Wang, J., Han, B., ValizadehAslani, T., Agbavor, F., Zhang, Y., Hu, M., Zhao, L., & Liang, H. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104533 · Cited by 32 · see citing works
Lorenzoni, G., Tavares, C., do Nascimento, N. M., Alencar, P., & Cowan, D. (2025) · PLoS ONE · doi:10.1371/journal.pone.0322299 · Cited by 20 · see citing works
A comprehensive survey for automatic text summarization: Techniques, approaches and perspectives
Luo, M., Xue, B., & Niu, B. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128280 · Cited by 21 · see citing works
Evaluation metrics on text summarization: comprehensive survey
Davoodijam, E., & Meybodi, M. A. (2024) · Knowledge and Information Systems · doi:10.1007/s10115-024-02217-0 · Cited by 20 · see citing works
Extractive text summarization for biomedical transcripts using deep dense LSTM‐CNN framework
Bedi, P. P. S., Bala, M., & Sharma, K. (2023) · Expert Systems · doi:10.1111/exsy.13490 · Cited by 17 · see citing works
Clustering-based topic modeling for biomedical documents extractive text summarization
AbdelAziz, N. M., Ali, A. A., Naguib, S. M., & Fayed, L. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06640-6 · Cited by 17 · see citing works
Graph-Based Extractive Text Summarization Sentence Scoring Scheme for Big Data Applications
Verma, J. P. V., Bhargav, S., Bhavsar, M. D., Bhattacharya, P., Bostani, A., Chowdhury, S., Webber, J. L., & Mehbodniya, A. (2023) · Information · doi:10.3390/info14090472 · Cited by 16 · see citing works
Pawar, D., Phansalkar, S., Sharma, A., Sahu, G. K., Ang, C. K., & Lim, W. H. (2023) · Sustainability · doi:10.3390/su15054216 · Cited by 13 · see citing works
Revolutionizing Healthcare with Generative Artificial Intelligence Technologies
Rashidieranjbar, F., Farhadi, A., & Zamanifar, A. (2025) · Information systems engineering and management · doi:10.1007/978-3-031-76710-4_10 · Cited by 15 · see citing works
de Oliveira, R. S., & Nascimento, E. G. S. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104477 · Cited by 12 · see citing works
Aftiss, A., Lamsiyah, S., Alaoui, S. O. E., & Schommer, C. (2024) · IEEE Access · doi:10.1109/access.2024.3514915 · Cited by 10 · see citing works
Review of ambiguity problem in text summarization using hybrid ACA and SLR
Sutriawan, S., Rustad, S., Shidik, G. F., Pujiono, P., & Muljono, M. (2024) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2024.200360 · Cited by 12 · see citing works
A Comparative Study and Analysis of Text Summarization Methods
Jony, A. I., Rithin, A. T., & Edrish, S. I. (2024) · Malaysian Journal of Science and Advanced Technology · doi:10.56532/mjsat.v4i2.231 · Cited by 6 · see citing works
Long-Text Abstractive Summarization using Transformer Models: A Systematic Review
Abubakar, B. S., Bichi, A. A., Mahmud, U., & Bello, A. M. (2025) · Journal of the Brazilian Computer Society · doi:10.5753/jbcs.2025.5786 · Cited by 5 · see citing works
Automatic Extractive Text Summarization of Hindi Text using Deep learning approach
Kapoor, N., Gupta, A. K., & Meenakshi, K. (2023) · International Conference on Computer Communication and Informatics (ICCCI) · doi:10.1109/iccci56745.2023.10128243 · Cited by 5 · see citing works
A systematic review of long document summarization methods: Evaluation metrics and approaches
Gana, B., Allende-Cid, H., Stefan Rüping, A., Becerra, M., & Zamora, J. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131287 · Cited by 4 · see citing works
Aligning Factual Consistency for Clinical Studies Summarization through Reinforcement Learning
Tang, X., Cohan, A., & Gerstein, M. (2023) · Clinical Natural Language Processing Workshop (ClinicalNLP) · doi:10.18653/v1/2023.clinicalnlp-1.7 · Cited by 4 · see citing works
NOVEL TRANSFORMER-BASED APPROACH ENHANCED BY REINFORCEMENT LEARNING AND ATTENTION MECHANISMS
Gamal, M., & Mostafa Gamal, H. F. A. H. (2023) · Journal of Southwest Jiaotong University · doi:10.35741/issn.0258-2724.58.6.5 · Cited by 4 · see citing works
A survey of automatic text summarization: concepts, advances and future prospects
Lv, C., Tang, Y., Ao, L., Huang, Y., Zhang, S., Fan, J., & Han, W. (2025) · International Journal of Speech Technology · doi:10.1007/s10772-025-10215-y · Cited by 4 · see citing works
Large Language Models for Explainable Medical Text Summarization: A Systematic Literature Review
Ayalew, A. M., Hasan, M. R., Seppänen, T., & Oussalah, M. (2026) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.70089 · Cited by 3 · see citing works
Pamungkas, Y. (2024) · Jurnal Sisfokom (Sistem Informasi dan Komputer) · doi:10.32736/sisfokom.v13i2.2117 · Cited by 3 · see citing works
Improving Text Summarization Quality by Combining T5-Based Models and Convolutional Seq2Seq Models
Lubis, A. R., Safitri, H. R., Irvan, I., Lubis, M., & Al-Khowarizmi, A.-K. (2023) · Journal of Applied Engineering and Technological Science (JAETS) · doi:10.37385/jaets.v5i1.2503 · Cited by 2 · see citing works
Large Language Models Evaluation for PubMed Extractive Summarisation
Xia, T., Bertini, F., & Montesi, D. (2025) · ACM Transactions on Computing for Healthcare · doi:10.1145/3766905 · Cited by 2 · see citing works
Nahed, P., Kambar, M. E. Z. N., & Taghva, K. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-3302-6_26 · Cited by 1 · see citing works
API Summarization, Digitization of PHR, and Validation Through Blockchain
Bose, S., Singh, R. K., Pathak, S., Sanghvi, R., & Vinodha, K. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-6318-4_32 · Cited by 1 · see citing works
WhaleOptNB: A Method for Automated Biomedical Text Document Classification
Chinnaiyan, V. K., Chohan, J. S., & Kalita, K. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-73324-6_18 · Cited by 1 · see citing works
Transformer-Based Abstract Generation of Medical Case Reports
Chandraju, A. V., & Gnanasigamani, L. J. (2022) · International Journal of Engineering and Advanced Technology · doi:10.35940/ijeat.a3853.1012122 · Cited by 1 · see citing works
Transformer-Based Text Highlighting for Medical Terms
Ozyegen, L., Çevik, M., & Basar, A. (2024) · IEEE International Conference on Collaborative Advances in Software and Computing (CASCON) · doi:10.1109/cascon62161.2024.10837964 · Cited by 1 · see citing works
Sorting submission text using Natural Language ProcessingPreprint
Ghelani, D. (2022) · doi:10.22541/au.166455373.35532713/v1
SSATS—Enhancement of Semantic Similarity of Abstractive Text Summarization Using Transformer
Thirisha, R., Kiruthiga, A. S., Arunkumar, S., & Lilian, J. F. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-5997-6_8
An attentive neural network based on deep learning approach for abstractive text summarization
Sapra, S. J., Thakur, S., Kapse, A. S., & Atique, M. (2024) · AIP conference proceedings · doi:10.1063/5.0239206
Rupesh, K., & Christy, S. (2024) · AIP conference proceedings · doi:10.1063/5.0233047
Study of State‐of‐the‐Art Performance Metrics in NLP
Sridhar, M., Irfan, M., & Kathirmani, S. (2025) · doi:10.1002/9781394302932.ch5
Enhancing Technical Documentation through Intelligent Text Summarization Techniques
Kaur, R. (2025) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2025.73052
Evaluating Text Summarization from Multiple Dimensions Using In-Context Learning
Sharma, K. K., Kumar, A., & Srivastava, A. K. (2025) · International Conference on Engineering Innovations and Technologies (ICoEIT) · doi:10.1109/icoeit63558.2025.11210872
Medical document summarization using text rank algorithm
Hajari, M., Shivaramakrishna, B., Manikanta, T. K., Pranay, A. S., Mudusu, R., Yelaki, P., & Hussein, R. R. (2025) · AIP conference proceedings · doi:10.1063/5.0298468
Incorporating moral motivation in automatic summary generation of literary fiction
Jiang, C., Jin, W., Wang, X., & Zhao, L. (2025) · Data and Information Management · doi:10.1016/j.dim.2025.100120
From data to diagnosis: a scoping review of NLP-based medical text summarisation methods
Dilani, K. (2025) · Информатика Экономика Управление - Informatics Economics Management · doi:10.47813/2782-5280-2025-4-4-2048-2055
The Role of LLMs for Optimizing Clinical Insights in Text Summarization
Camran, A., & Khan, A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-10670-4_14
Sahithi, A., Rashmita, V., Haritha, D. S. N., Sahithi, N. S., & Carie, A. (2025) · International Conference on Intelligent Systems and Embedded Design (ISED) · doi:10.1109/ised67359.2025.11404927
Kangralkar, V., Math, M. M., Patil, U., Kori, S. M., Bhat, N. S., Kalghatgi, S. M., & Sambrekar, K. P. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-19690-3_43
Multimodal Sensing System for Health Monitoring: A Systematic Review
Awa, M. N., & García-Zapirain, B. (2026) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-032-18188-6_17
Sharma, N., & Singh, S. (2025) · New Review of Information Networking · doi:10.1080/13614576.2025.2598233
Large Language Model: Future of Healthcare Research With Challenges
Heyat, M. B. B., Rehman, A. U., Zeeshan, H. M., Hayat, M. A. B., Akhtar, F., Sadaf, Ansari, M. A., Wang, L., Lai, D., Prasath, V. B. S., et al. (2026) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.70103
Bilimsel Metinlerde Anlamsal Özetleme: Derin Öğrenme Yaklaşımlarının Performans Analizi
Özcan, S., & Özcan, A. (2026) · Bilişim Teknolojileri Dergisi · doi:10.17671/gazibtd.1863483
Baştürk, B., & Onan, A. (2026) · Bilişim Teknolojileri Dergisi · doi:10.17671/gazibtd.1846644
Shayakhmetova, A., Sambetbayeva, M., Барахнин, В. Б., Sultangaziyeva, A., Мукажанов, Н., Batyrkhanov, A., Serikbayeva, S., & Begim, R. (2026) · Information · doi:10.3390/info17090825
Sivagnanam, B., Rajiakodi, S., Abdulkader, R. S., & Chakravarthi, B. R. (2026) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-026-00876-3
Baştürk, B., & Onan, A. (2026) · Sakarya University Journal of Computer and Information Sciences · doi:10.35377/saucis...1780353
- Authors
- Chaves, A., Kesiku, C., & Garcia-Zapirain, B.
- Affiliations
- eVida Research Group, University of Deusto, Avda/Universidades 24, 48007 Bilbao, Spain
- Universidad de Deusto (ES)
- Venue
- Information
- Publisher
- MDPI AG
- Published
- 2022-08-19
- Volume
- 13
- Issue
- 8
- Pages
- 393
- ISSN
- 2078-2489 (ISSN-L)
- OpenAlex
- W4298339612
- Cited by
- 53
- References
- 70
- Citations by year
- 2026: 10 · 2025: 16 · 2024: 15 · 2023: 10 · 2022: 2
- FWCI
- 0.76
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Information (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- text summarization, biomedical text, extractive summarization, abstractive summarization, single-document summarization, biomedical literature, transformer-based summarization, medical information retrieval
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
