{"type":"ScholarlyArticle","title":"MEDVOC: Vocabulary Adaptation for Fine-tuning Pre-trained Language Models on Medical Text Summarization","authors":[{"name":"Gunjan Balde","slug":"gunjan-balde","given":"Gunjan","family":"Balde","affiliation":"Indian Institute of Technology Kharagpur"},{"name":"Soumyadeep Roy","slug":"soumyadeep-roy","given":"Soumyadeep","family":"Roy","affiliation":"Indian Institute of Technology Kharagpur; L3S Research Center , Germany"},{"name":"Mainack Mondal","slug":"mainack-mondal","given":"Mainack","family":"Mondal","affiliation":"Indian Institute of Technology Kharagpur"},{"name":"Niloy Ganguly","slug":"niloy-ganguly","given":"Niloy","family":"Ganguly","affiliation":"Indian Institute of Technology Kharagpur"}],"abstract":"This work presents a dynamic vocabulary adaptation strategy, MEDVOC, for fine-tuning pre-trained language models (PLMs) like BertSumAbs, BART, and PEGASUS for improved medical text summarization. In contrast to existing domain adaptation approaches in summarization, MEDVOC treats vocabulary as an optimizable parameter  and optimizes the PLM vocabulary based on fragment score conditioned only on the downstream task's reference summaries. Unlike previous works on vocabulary adaptation (limited only to classification tasks), optimizing vocabulary based on summarization tasks requires an extremely costly intermediate fine-tuning step on large summarization datasets. To that end, our novel fragment score-based hyperparameter search very significantly reduces this fine-tuning time --- from 450 days to less than 2 days on average. Furthermore, while previous works on vocabulary adaptation are often primarily tied to single PLMs, MEDVOC is designed to be deployable across multiple PLMs (with varying model vocabulary sizes, pre-training objectives, and model sizes) --- bridging the limited vocabulary overlap between the biomedical literature domain and PLMs. MEDVOC outperforms baselines by 15.74% in terms of Rouge-L in zero-shot setting and shows gains of 17.29% in high Out-Of-Vocabulary (OOV) concentrations.  Our human evaluation shows MEDVOC generates more faithful medical summaries (88% compared to 59% in baselines).","doi":"10.24963/ijcai.2024/683","url":"https://ijcai.org/proceedings/2024/683","pdf":"https://ijcai.org/proceedings/2024/0683.pdf","markdown":"https://ijcai.org/proceedings/2024/683.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/683","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Natural Language Processing","track":"Main Track","number":683,"pages":{"first":6180,"last":6188},"keywords":["Multidisciplinary Topics and Applications: MTA: Health and medicine","Machine Learning: ML: Generative models","Natural Language Processing: NLP: Language generation","Natural Language Processing: NLP: Summarization"],"videos":[],"references":[{"position":1,"raw":"[Alambo et al., 2022] Amanuel Alambo, Tanvi Banerjee, et al. Improving the factual accuracy of abstractive clin- ical text summarization using multi-objective optimiza- tion. In 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pages 1615-1618. IEEE, 2022.","title":"Improving the factual accuracy of abstractive clinical text summarization using multi-objective optimization","authors":"Amanuel Alambo, Tanvi Banerjee","year":"2022","venue":"44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Allison et al., 2006] Ben Allison, David Guthrie, et al. An- other look at the data sparsity problem. In Text, Speech and Dialogue: 9th International Conference, TSD 2006, Brno, Czech Republic, September 11-15, 2006. Proceedings 9, pages 327-334. Springer, 2006.","title":"Another look at the data sparsity problem","authors":"Ben Allison, David Guthrie","year":"2006","venue":"Text, Speech and Dialogue: 9th International Conference, TSD 2006","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Ben Abacha and Demner-Fushman, 2019] Asma Ben Abacha and Dina Demner-Fushman. On the summarization of consumer health questions. In Proceed- ings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2228-2234, July 2019.","title":"On the summarization of consumer health questions","authors":"Asma Ben, Abacha, Dina Demner-Fushman","year":"2019","venue":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chang and Lu, 2021] Ting-Yun Chang and Chi-Jen Lu. Re- thinking why intermediate-task fine-tuning works. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 706-713, November 2021.","title":"Rethinking why intermediate-task fine-tuning works","authors":"Ting-Yun Chang, Chi-Jen Lu","year":"2021","venue":"Findings of the Association for Computational Linguistics: EMNLP 2021","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chen et al., 2023] Zeming Chen, Alejandro Hernández Cano, et al. Meditron-70b: Scaling medical pretraining for large language models. arXiv preprint arXiv:2311.16079, 2023.","title":"Meditron-70b: Scaling medical pretraining for large language models","authors":"Chen","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Dai et al., 2021] Songtai Dai, Quan Wang, et al. BDKG at MEDIQA 2021: System report for the radiology report summarization task. In Proceedings of the 20th Workshop on Biomedical Language Processing, pages 103-111, June 2021.","title":"BDKG at MEDIQA 2021: System report for the radiology report summarization task","authors":"Dai","year":"2021","venue":"Proceedings of the 20th Workshop on Biomedical Language Processing","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Diao et al., 2021] Shizhe Diao, Ruijia Xu, et al. Taming pre-trained language models with n-gram representations for low-resource domain adaptation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 3336-3349, August 2021.","title":"Taming pre-trained language models with n-gram representations for low-resource domain adaptation","authors":"Diao","year":"2021","venue":"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Fabbri et al., 2021a] Alexander Fabbri, Simeng Han, et al. Improving zero and few-shot abstractive summarization with intermediate fine-tuning and data augmentation. In Proceedings of the 2021 Conference of the North Amer- ican Chapter of the Association for Computational Lin- guistics: Human Language Technologies, pages 704-717, June 2021.","title":"Improving zero and few-shot abstractive summarization with intermediate fine-tuning and data augmentation","authors":"Fabbri","year":"2021","venue":"Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Fabbri et al., 2021b] Alexander R. Fabbri, Wojciech Kryściński, et al. SummEval: Re-evaluating summa- rization evaluation. Transactions of the Association for Computational Linguistics, 9:391-409, 2021.","title":"SummEval: Re-evaluating summarization evaluation","authors":"Fabbri","year":"2021","venue":"Transactions of the Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Gu et al., 2021] Yu Gu, Robert Tinn, Cheng, et al. Domain- specific language model pretraining for biomedical natural language processing. ACM Trans. Comput. Healthcare, 3(1), oct 2021.","title":"Domainspecific language model pretraining for biomedical natural language processing","authors":"Yu Gu, Robert Tinn, Cheng","year":"2021","venue":"ACM Trans. Comput. Healthcare","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[He et al., 2021] Yifan He, Mosha Chen, et al. damo nlp at MEDIQA 2021: Knowledge-based preprocessing and coverage-oriented reranking for medical question summa- rization. In Proceedings of the 20th Workshop on Biomed- ical Language Processing, pages 112-118, June 2021.","title":"damo nlp at MEDIQA 2021: Knowledge-based preprocessing and coverage-oriented reranking for medical question summarization","authors":"Yifan He, Mosha Chen","year":"2021","venue":"Proceedings of the 20th Workshop on Biomedical Language Processing","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Hong et al., 2021] Jimin Hong, TaeHee Kim, et al. AVo- caDo: Strategy for adapting vocabulary to downstream domain. In Proceedings of the 2021 Conference on Em- pirical Methods in Natural Language Processing, pages 4692-4700, November 2021.","title":"AVo-caDo: Strategy for adapting vocabulary to downstream domain","authors":"Jimin Hong, Taehee Kim","year":"2021","venue":"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Jin et al., 2019] Qiao Jin, Bhuwan Dhingra, et al. Pub- MedQA: A dataset for biomedical research question an- swering. In Proceedings of the EMNLP-IJCNLP 2019, pages 2567-2577, November 2019.","title":"Pub-MedQA: A dataset for biomedical research question answering","authors":"Qiao Jin, Bhuwan Dhingra","year":"2019","venue":"Proceedings of the EMNLP-IJCNLP 2019","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Kanwal and Rizzo, 2022] Neel Kanwal and Giuseppe Rizzo. Attention-based clinical note summarization. In Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing, SAC '22, page 813-820, 2022.","title":"Attention-based clinical note summarization","authors":"Neel Kanwal, Giuseppe Rizzo","year":"2022","venue":"Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing, SAC '22","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Lamproudis. et al., 2022] Anastasios Lamproudis., Aron Henriksson., et al. Vocabulary modifications for domain- adaptive pretraining of clinical language models. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) -HEALTHINF, pages 180-188. IN- STICC, 2022.","title":"Vocabulary modifications for domainadaptive pretraining of clinical language models","authors":"Anastasios Lamproudis, Aron Henriksson","year":"2022","venue":"Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) -HEALTHINF","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Laskar et al., 2022] Md Tahmid Rahman Laskar, Enamul Hoque, et al. Domain Adaptation with Pre-trained Trans- formers for Query-Focused Abstractive Text Summariza- tion. Computational Linguistics, 48(2):279-320, 06 2022.","title":"Domain Adaptation with Pre-trained Transformers for Query-Focused Abstractive Text Summarization","authors":"Md Tahmid Rahman Laskar, Enamul Hoque","year":"2022","venue":"Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Lee et al., 2019] Jinhyuk Lee, Wonjin Yoon, et al. BioBERT: a pre-trained biomedical language representa- tion model for biomedical text mining. Bioinformatics, 36(4):1234-1240, 09 2019.","title":"BioBERT: a pre-trained biomedical language representation model for biomedical text mining","authors":"Lee","year":"2019","venue":"Bioinformatics","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Lewis et al., 2020] Mike Lewis, Yinhan Liu, et al. Bart: De- noising sequence-to-sequence pre-training for natural lan- guage generation, translation, and comprehension. In Pro- ceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7871-7880, 2020.","title":"Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension","authors":"Mike Lewis, Yinhan Liu","year":"2020","venue":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Lin, 2004] Chin-Yew Lin. ROUGE: A package for auto- matic evaluation of summaries. In Text Summarization Branches Out, pages 74-81, 2004.","title":"ROUGE: A package for automatic evaluation of summaries","authors":"Chin-Yew Lin","year":"2004","venue":"Text Summarization Branches Out","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Liu and Lapata, 2019] Yang Liu and Mirella Lapata. Text summarization with pretrained encoders. In Proceedings of EMNLP-IJCNLP, pages 3730-3740, 2019.","title":"Text summarization with pretrained encoders","authors":"Yang Liu, Mirella Lapata","year":"2019","venue":"Proceedings of EMNLP-IJCNLP","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Liu et al., 2023] Siyang Liu, Naihao Deng, et al. Task- adaptive tokenization: Enhancing long-form text genera- tion efficacy in mental health and beyond. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 15264-15281, 2023.","title":"Taskadaptive tokenization: Enhancing long-form text generation efficacy in mental health and beyond","authors":"Liu","year":"2023","venue":"Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Mollá and Santiago-Martínez, 2011] Diego Mollá and María Elena Santiago-Martínez. Development of a corpus for evidence based medicine summarisation. In Proceedings of the Australasian Language Technology Association Workshop, pages 86-94, 2011.","title":"Diego Mollá and María Elena Santiago-Martínez. Development of a corpus for evidence based medicine summarisation","authors":"Santiago-Martínez Mollá","year":"2011","venue":"Proceedings of the Australasian Language Technology Association Workshop","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Phang et al., 2019] Jason Phang, Thibault Févry, et al. Sen- tence encoders on stilts: Supplementary training on inter- mediate labeled-data tasks, 2019.","title":"Sentence encoders on stilts: Supplementary training on intermediate labeled-data tasks","authors":"Jason Phang, Thibault Févry","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Radford et al., 2019] Alec Radford, Jeffrey Wu, et al. Lan- guage models are unsupervised multitask learners. OpenAI blog, 1(8):9, 2019.","title":"Language models are unsupervised multitask learners","authors":"Alec Radford, Jeffrey Wu","year":"2019","venue":"OpenAI blog","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Rust et al., 2021] Phillip Rust, Jonas Pfeiffer, et al. How good is your tokenizer? on the monolingual performance of multilingual language models. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 3118-3135, August 2021.","title":"How good is your tokenizer? on the monolingual performance of multilingual language models","authors":"Phillip Rust, Jonas Pfeiffer","year":"2021","venue":"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Schick and Schütze, 2020] Timo Schick and Hinrich Schütze. Rare words: A major problem for contextualized embeddings and how to fix it by attentive mimicking. Pro- ceedings of the AAAI Conference on Artificial Intelligence, 34(05):8766-8774, Apr. 2020.","title":"Rare words: A major problem for contextualized embeddings and how to fix it by attentive mimicking","authors":"Schütze Schick, Timo Schick, Hinrich Schütze","year":"2020","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[See et al., 2017] Abigail See, Peter J. Liu, et al. Get to the point: Summarization with pointer-generator networks. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Volume 1: Long Papers, pages 1073-1083, 2017.","title":"Get to the point: Summarization with pointer-generator networks","authors":"Abigail See, Peter J Liu","year":"2017","venue":"Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Soldaini and Goharian, 2016] Luca Soldaini and Nazli Go- harian. Quickumls: a fast, unsupervised approach for med- ical concept extraction. In MedIR workshop, SIGIR, pages 1-4, 2016.","title":"Luca Soldaini and Nazli Goharian. Quickumls: a fast, unsupervised approach for medical concept extraction","authors":"Goharian Soldaini","year":"2016","venue":"MedIR workshop, SIGIR","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Suresh et al., 2023] Shilpa Suresh, Nazgol Tavabi, et al. Intermediate domain finetuning for weakly supervised domain-adaptive clinical NER. In The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, pages 320-325, July 2023.","title":"Intermediate domain finetuning for weakly supervised domain-adaptive clinical NER","authors":"Shilpa Suresh, Nazgol Tavabi","year":"2023","venue":"The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Tai et al., 2020] Wen Tai, H. T. Kung, et al. exBERT: Ex- tending pre-trained models with domain-specific vocabu- lary under constrained training resources. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1433-1439, November 2020.","title":"exBERT: Extending pre-trained models with domain-specific vocabulary under constrained training resources","authors":"Wen Tai, H T Kung","year":"2020","venue":"Findings of the Association for Computational Linguistics: EMNLP 2020","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Tsatsaronis et al., 2015] George Tsatsaronis, Georgios Ba- likas, et al. An overview of the bioasq large-scale biomed- ical semantic indexing and question answering competi- tion. BMC Bioinformatics, 16:138, 2015.","title":"An overview of the bioasq large-scale biomedical semantic indexing and question answering competition","authors":"George Tsatsaronis, Georgios Balikas","year":"2015","venue":"BMC Bioinformatics","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Wu et al., 2023] Chaoyi Wu, Weixiong Lin, et al. Pmc- llama: Towards building open-source language models for medicine, 2023.","title":"Pmcllama: Towards building open-source language models for medicine","authors":"Wu","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Xie et al., 2022] Qianqian Xie, Jennifer Amy Bishop, et al. Pre-trained language models with domain knowledge for biomedical extractive summarization. Knowledge-Based Systems, 252:109460, 2022.","title":"Pre-trained language models with domain knowledge for biomedical extractive summarization","authors":"Qianqian Xie, Jennifer Amy Bishop","year":"2022","venue":"Knowledge-Based Systems","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Xie et al., 2023a] Qianqian Xie, Jinpeng Hu, et al. Factr- eranker: Fact-guided reranker for faithful radiology report summarization. arXiv preprint arXiv:2303.08335, 2023.","title":"Factreranker: Fact-guided reranker for faithful radiology report summarization","authors":"Xie","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Xie et al., 2023b] Qianqian Xie, Zheheng Luo, et al. A sur- vey for biomedical text summarization: From pre-trained to large language models, 2023.","title":"A survey for biomedical text summarization: From pre-trained to large language models","authors":"Xie","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Xu et al., 2021] Jingjing Xu, Hao Zhou, et al. Vocabulary learning via optimal transport for neural machine transla- tion. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Pro- cessing (Volume 1: Long Papers), pages 7361-7373, Au- gust 2021.","title":"Vocabulary learning via optimal transport for neural machine translation","authors":"Jingjing Xu, Hao Zhou","year":"2021","venue":"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Xu et al., 2023] Benfeng Xu, Chunxu Zhao, et al. Retrieval- augmented domain adaptation of language models. In Pro- ceedings of the 8th Workshop on Representation Learning for NLP (RepL4NLP 2023), pages 54-64, July 2023.","title":"Retrievalaugmented domain adaptation of language models","authors":"Xu","year":"2023","venue":"Proceedings of the 8th Workshop on Representation Learning for NLP (RepL4NLP 2023)","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Yadav et al., 2022] Shweta Yadav, Deepak Gupta, et al. Chq-summ: A dataset for consumer healthcare question summarization. arXiv preprint arXiv:2206.06581, 2022.","title":"Chq-summ: A dataset for consumer healthcare question summarization","authors":"Shweta Yadav, Deepak Gupta","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Yuan et al., 2022] Hongyi Yuan, Zheng Yuan, et al. Bio- BART: Pretraining and evaluation of a biomedical genera- tive language model. In Proceedings of the 21st Workshop on Biomedical Language Processing, pages 97-109, May 2022.","title":"Bio-BART: Pretraining and evaluation of a biomedical generative language model","authors":"Hongyi Yuan, Zheng Yuan","year":"2022","venue":"Proceedings of the 21st Workshop on Biomedical Language Processing","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Zhang et al., 2020a] Jingqing Zhang, Yao Zhao, et al. PE- GASUS: Pre-training with extracted gap-sentences for ab- stractive summarization. In Proceedings of the 37th Inter- national Conference on Machine Learning, volume 119 of Proceedings of Machine Learning Research, pages 11328-11339, 13-18 Jul 2020.","title":"PE-GASUS: Pre-training with extracted gap-sentences for abstractive summarization","authors":"Zhang","year":"2020","venue":"Proceedings of the 37th International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Zhang et al., 2020b] Tianyi Zhang, Varsha Kishore, et al. Bertscore: Evaluating text generation with BERT. In 8th International Conference on Learning Representations, ICLR, 2020.","title":"Bertscore: Evaluating text generation with BERT","authors":"Zhang","year":"2020","venue":"8th International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Zhang et al., 2021] Longxiang Zhang, Renato Negrinho, et al. Leveraging pretrained models for automatic summa- rization of doctor-patient conversations. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 3693-3712, November 2021.","title":"Leveraging pretrained models for automatic summarization of doctor-patient conversations","authors":"Zhang","year":"2021","venue":"Findings of the Association for Computational Linguistics: EMNLP 2021","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Zhang et al., 2023a] Kai Zhang, Jun Yu, et al. Biomedgpt: A unified and generalist biomedical generative pre-trained transformer for vision, language, and multimodal tasks. arXiv preprint arXiv:2305.17100, 2023.","title":"Biomedgpt: A unified and generalist biomedical generative pre-trained transformer for vision, language, and multimodal tasks","authors":"Zhang","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Zhang et al., 2023b] Nan Zhang, Yusen Zhang, et al. Fame- summ: Investigating and improving faithfulness of med- ical summarization. arXiv preprint arXiv:2311.02271, 2023.","title":"Famesumm: Investigating and improving faithfulness of medical summarization","authors":"Zhang","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}