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Ascle: A Python Natural Language Processing Toolkit for Medical Text Generation

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arxiv 2311.16588 v2 pith:N4PQVB3I submitted 2023-11-28 cs.CL

Rui Yang , Qingcheng Zeng , Keen You , Yujie Qiao , Lucas Huang , Chia-Chun Hsieh , Benjamin Rosand , Jeremy Goldwasser
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Amisha D Dave Tiarnan D.L. Keenan Emily Y Chew Dragomir Radev Zhiyong Lu Hua Xu Qingyu Chen Irene Li
This is my paper · ORCID
classification cs.CL
keywords ascletextlanguagetoolkitfunctionsgenerationmedicalmodels
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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