Yongbin Jeong


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Teddysum at MEDIQA-Chat 2023: an analysis of fine-tuning strategy for long dialog summarization
Yongbin Jeong | Ju-Hyuck Han | Kyung Min Chae | Yousang Cho | Hyunbin Seo | KyungTae Lim | Key-Sun Choi | Younggyun Hahm
Proceedings of the 5th Clinical Natural Language Processing Workshop

In this paper, we introduce the design and various attempts for TaskB of MEDIQA-Chat 2023. The goal of TaskB in MEDIQA-Chat 2023 is to generate full clinical note from doctor-patient consultation dialogues. This task has several challenging issues, such as lack of training data, handling long dialogue inputs, and generating semi-structured clinical note which have section heads. To address these issues, we conducted various experiments and analyzed their results. We utilized the DialogLED model pre-trained on long dialogue data to handle long inputs, and we pre-trained on other dialogue datasets to address the lack of training data. We also attempted methods such as using prompts and contrastive learning for handling sections. This paper provides insights into clinical note generation through analyzing experimental methods and results, and it suggests future research directions.


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Enhancing Quality of Corpus Annotation: Construction of the Multi-Layer Corpus Annotation and Simplified Validation of the Corpus Annotation
Youngbin Noh | Kuntae Kim | Minho Lee | Cheolhun Heo | Yongbin Jeong | Yoosung Jeong | Younggyun Hahm | Taehwan Oh | Hyonsu Choe | Seokwon Park | Jin-Dong Kim | Key-Sun Choi
Proceedings of the 34th Pacific Asia Conference on Language, Information and Computation