Abstract
This paper summarizes two approaches developed for BioNLP2023 workshop task 1A: clinical problem list summarization. We develop two types of methods with either rules or pre-trained language models. In the rule-based summarization model, we leverage UMLS (Unified Medical Language System) and a negation detector to extract text spans to represent the summary. We also fine tune three pre-trained language models (BART, T5 and GPT2) to generate the summaries. Experiment results show the rule based system returns extractive summaries but lower ROUGE-L score (0.043), while the fine tuned T5 returns a higher ROUGE-L score (0.208).- Anthology ID:
- 2023.bionlp-1.47
- Volume:
- The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Dina Demner-fushman, Sophia Ananiadou, Kevin Cohen
- Venue:
- BioNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 491–496
- Language:
- URL:
- https://aclanthology.org/2023.bionlp-1.47
- DOI:
- 10.18653/v1/2023.bionlp-1.47
- Cite (ACL):
- Ming Liu, Dan Zhang, Weicong Tan, and He Zhang. 2023. DeakinNLP at ProbSum 2023: Clinical Progress Note Summarization with Rules and Language ModelsClinical Progress Note Summarization with Rules and Languague Models. In The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, pages 491–496, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- DeakinNLP at ProbSum 2023: Clinical Progress Note Summarization with Rules and Language ModelsClinical Progress Note Summarization with Rules and Languague Models (Liu et al., BioNLP 2023)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2023.bionlp-1.47.pdf