uMedSum: A Unified Framework for Clinical Abstractive Summarization
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Aishik Nagar, Yutong Liu, Andy T. Liu, Viktor Schlegel, Vijay Prakash Dwivedi, Arun-Kumar Kaliya-Perumal, Guna Pratheep Kalanchiam, Yili Tang, Robby T. Tan. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.
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For inference-only deployments, smaller models now achieve comparable performance for many clinical information retrieval and summarization tasks.
“For inference-only deployments, smaller models now achieve comparable performance for many clinical information retrieval and summarization tasks [32].”
From Implementation of large language models in electronic health records (2025) · cites this paper as [32]
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Research works printed but not linked: 23
- Asma Ben Abacha and Dina Demner-Fushman. 2019. On the summarization of consumer health questions. In Proceedings of the 57th Annual Meeting of the As- sociation for Computational Linguistics, pages 2228- 2234.
- G Addams, A Fabbri, F Ladhak, E Lehman, and N El- hadad. 2023. From sparse to dense: Gpt-4 summa- rization with chain of density prompting. arxiv.
- Alistair EW Johnson, Tom J Pollard, Lu Shen, Li-wei H Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark. 2016. Mimic-iii, a freely accessi- ble critical care database. Scientific data, 3(1):1-9.
- Philippe Laban, Tobias Schnabel, Paul N Bennett, and Marti A Hearst. 2022. Summac: Re-visiting nli- based models for inconsistency detection in summa- rization. Transactions of the Association for Compu- tational Linguistics, 10:163-177.
- Chin-Yew Lin. 2004. Rouge: A package for automatic evaluation of summaries. In Text summarization branches out, pages 74-81.
- Wei Liu, Wanyao Shi, Zijian Zhang, and Hui Huang. 2024. Hit-mi&t lab at semeval-2024 task 6: Deberta- based entailment model is a reliable hallucination detector. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 1788-1797.
- Varun Nathan, Ayush Kumar, and Jithendra Vepa. 2023. Investigating the role and impact of disfluency on summarization. In Proceedings of the 2023 Con- ference on Empirical Methods in Natural Language Processing: Industry Track, pages 541-551.
- Ani Nenkova, Kathleen McKeown, et al. 2011. Auto- matic summarization. Foundations and Trends® in Information Retrieval, 5(2-3):103-233.
- Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32.
- Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9.
- Mathieu Ravaut, Shafiq Joty, Aixin Sun, and Nancy F Chen. 2023. On context utilization in summarization with large language models. arXiv e-prints, pages arXiv-2310.
- Nils Reimers and Iryna Gurevych. 2019. Sentence-bert: Sentence embeddings using siamese bert-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. Associa- tion for Computational Linguistics.
- Nipun Sadvilkar and Mark Neumann. 2020. Pysbd: Pragmatic sentence boundary disambiguation. arXiv preprint arXiv:2010.09657. · arXiv
- Jill Sammet and Ralf Krestel. 2023. Domain-specific keyword extraction using bert. In Proceedings of the 4th Conference on Language, Data and Knowledge, pages 659-665.
- Viktor Schlegel, Hao Li, Yuping Wu, Anand Sub- ramanian, Thanh-Tung Nguyen, Abhinav Ramesh Kashyap, Daniel Beck, Xiaojun Zeng, Riza Theresa Batista-Navarro, Stefan Winkler, and Goran Nenadic. 2023. PULSAR at MEDIQA-Sum 2023: Large Lan- guage Models Augmented by Synthetic Dialogue Convert Patient Dialogues to Medical Records. In Proceedings of the 14th International Conference of the CLEF Association (CLEF 2023), Thessaloniki. Springer Lecture Notes in Computer Science LNCS.
- Karan Singhal, Shekoofeh Azizi, Tao Tu, S Sara Mah- davi, Jason Wei, Hyung Won Chung, Nathan Scales, Ajay Tanwani, Heather Cole-Lewis, Stephen Pfohl, et al. 2023. Large language models encode clinical knowledge. Nature, 620(7972):172-180.
- Mingyang Song, Yi Feng, and Liping Jing. 2023. A survey on recent advances in keyphrase extraction from pre-trained language models. Findings of the Association for Computational Linguistics: EACL 2023, pages 2153-2164.
- Joe Stacey, Pasquale Minervini, Haim Dubossarsky, Oana-Maria Camburu, and Marek Rei. 2023. Log- ical reasoning for natural language inference us- ing generated facts as atoms. arXiv preprint arXiv:2305.13214. · arXiv
- Dave Van Veen, Cara Van Uden, Louis Blanke- meier, Jean-Benoit Delbrouck, Asad Aali, Christian Bluethgen, Anuj Pareek, Malgorzata Polacin, Ed- uardo Pontes Reis, Anna Seehofnerová, et al. 2024. Adapted large language models can outperform med- ical experts in clinical text summarization. Nature medicine, 30(4):1134-1142.
- Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pier- ric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020. Transform- ers: State-of-the-art natural language processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 38-45, Online. Association for Computational Linguistics.
- Wen-wai Yim, Yujuan Fu, Asma Ben Abacha, Neal Snider, Thomas Lin, and Meliha Yetisgen. 2023. Aci- bench: a novel ambient clinical intelligence dataset for benchmarking automatic visit note generation. Scientific Data, 10(1):586.
- Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Pe- ter Liu. 2020. Pegasus: Pre-training with extracted gap-sentences for abstractive summarization. In In- ternational conference on machine learning, pages 11328-11339.
- PMLR.
Cited by
Sorted by citations
Implementation of large language models in electronic health records
Griot, M., Vanderdonckt, J., & Yuksel, D. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0001141 · Cited by 23 · see citing works
A Modular Hybrid Transformer Framework for Trustworthy Radiology Report Summarization
Pourgholamali, S., Patti, E., & Aliberti, A. (2026) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai68641.2026.11536598
- Authors
- Nagar, A., Liu, Y., Liu, A. T., Schlegel, V., Dwivedi, V. P., Kaliya-Perumal, A.-K., Kalanchiam, G. P., Tang, Y., & Tan, R. T.
- Affiliations
- Nanyang Technological University (SG)
- Rehabilitation Research Institute of Singapore (SG)
- Tan Tock Seng Hospital (SG)
- ORCID
- Venue
- Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2025
- Pages
- 2653-2672
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W4412886762
- Cited by
- 2
- References
- 0
- Citations by year
- 2026: 1 · 2025: 1
- FWCI
- 7.83
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- Funders
- National Research Foundation
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- clinical text summarization
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