DeakinNLP at ProbSum 2023: Clinical Progress Note Summarization with Rules and Language ModelsClinical Progress Note Summarization with Rules and Languague Models
Ming Liu, Dan Zhang, Weicong Tan, He Zhang · 2023
This paper summarizes two approaches developed for BioNLP2023 workshop task 1A 1 : clinical progress note 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).