@inproceedings{Sotudeh2023OntGBart,
author = {Sotudeh, Sajad and Goharian, Nazli},
title = {OntG-Bart: Ontology-Infused Clinical Abstractive Summarization},
year = {2023},
isbn = {9798400700279},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3573128.3609346},
doi = {10.1145/3573128.3609346},
abstract = {Automating the process of clinical text summarization could save clinicians' reading time and reduce their fatigue, acknowledging the necessity of human professionals in the loop. This paper addresses clinical text summarization, aiming to incorporate ontology concept relationships via a Graph Neural Network (GNN) into the summarization process. Specifically, we propose a model, extending Bart's encoder-decoder framework with GNN encoder and multi-head attentional layers for decoder, producing ontology-aware summaries. This GNN interacts with the textual encoder, influencing their mutual representations. The model's effectiveness is validated on two real-world radiology datasets. We also present an ablation study to elucidate the impact of varied graph configurations and an error analysis aimed at pinpointing potential areas for future improvements.},
booktitle = {Proceedings of the ACM Symposium on Document Engineering 2023},
articleno = {17},
numpages = {4},
keywords = {text summarization, neural networks, clinical text summarization, abstractive summarization},
location = {Limerick, Ireland},
series = {DocEng '23}
}