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

Rui Yang, Qingcheng Zeng, Keen You, Yujie Qiao, Lucas Huang +11 more
11/28/2023
cs.CL

No official code found. Best implementation: Yale-LILY/Ascle (83 stars, last commit 2025-05-24).

Abstract

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.

View on arXivView PDF

Code Implementations(1)

Yale-LILY/AscleCommunity100%

A Python Natural Language Processing Toolkit for Medical Text Generation

8311Shell, PythonApr 7, 20221 years ago

Similar Papers

A Study of Generative Large Language Model for Medical Research and Healthcare

88% match1 repo452023

An Analysis on Large Language Models in Healthcare: A Case Study of BioBERT

87% match2023

Natural Language Generation in Healthcare: A Review of Methods and Applications

87% match2025

A Survey for Biomedical Text Summarization: From Pre-trained to Large Language Models

86% match2023

PMC-LLaMA: Towards Building Open-source Language Models for Medicine

86% match1 repo6802023

HuaTuo: Tuning LLaMA Model with Chinese Medical Knowledge

86% match1 repo5.0k2023

Cite this paper

@article{yang2023ascle,
  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},
  year   = {2023},
  eprint = {2311.16588},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2311.16588v2}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2311.16588.svg)](https://codeofpaper.com/paper/2311.16588)

Discussion