Medical Reports Summarization Using Text-To-Text Transformer
2 Citations•2023•
A. Helwan, Danielle Azar, D. Ozsahin
2023 Advances in Science and Engineering Technology International Conferences (ASET)
This work proposes a fine-tuned Text-to-Text Transformer (T5) to summarize medical reports and trains and test the model on the publicly available Indiana Dataset, and evaluates it using the ROUGE set of metrics.
Abstract
Summarization of medical reports in order to make them accessible to the large public is an important task that can highly benefit from the recent emergence of the deep learning and large language models (LLM). In this work, we propose a fine-tuned Text-to-Text Transformer (T5) to summarize such reports. We train and test our model on the publicly available Indiana Dataset. We evaluate it using the ROUGE set of metrics. The obtained results are promising.