This study introduces Ascle, a pioneering natural language processing (NLP) toolkit designed for medical text generation. Ascle is tailored for biomedical researchers and healthcare professionals with an easy-to-use, all-in-one solution that requires minimal programming expertise. For the first time, Ascle evaluates and provides interfaces for the latest pre-trained language models, encompassing four advanced and challenging generative functions: question-answering, text summarization, text simplification, and machine translation. In addition, Ascle integrates 12 essential NLP functions, along with query and search capabilities for clinical databases. The toolkit, its models, and associated data are publicly available via https://github.com/Yale-LILY/MedGen.
@article{arxiv.2311.16588,
title = {Ascle: A Python Natural Language Processing Toolkit for Medical Text Generation},
author = {Rui Yang and Qingcheng Zeng and Keen You and Yujie Qiao and Lucas Huang and Chia-Chun Hsieh and Benjamin Rosand and Jeremy Goldwasser and Amisha D Dave and Tiarnan D. L. Keenan and Emily Y Chew and Dragomir Radev and Zhiyong Lu and Hua Xu and Qingyu Chen and Irene Li},
journal= {arXiv preprint arXiv:2311.16588},
year = {2023}
}