Welcome to AbstractEase, a sophisticated tool designed to enhance the accessibility of PubMed abstracts through the application of advanced Natural Language Processing (NLP) techniques. This project employs state-of-the-art text classification algorithms to demystify the intricate language commonly found in scientific literature, providing users with clear and concise summaries.
AbstractEase serves as an invaluable resource for individuals across various domains, including researchers, students, and science enthusiasts. Our mission is to facilitate a deeper understanding of scientific literature by eliminating the need for laborious deciphering of complex terminology.
AbstractEase leverages text classification algorithms to identify and extract vital information from PubMed abstracts with precision.
Experience the convenience of clear and concise summaries with subheadings for easier understanding and saving the time spent on reading unnecessary jargon.
This uses the PubMed RCT 50K (Remote Controlled Trials) dataset (https://github.com/Franck-Dernoncourt/pubmed-rct.git) which contains various PubMed abstracts.
This project is licensed under the MIT License.
Feel free to leave a comment and make pull requests to help improve the model! Thanks for exploring!