Extractive Text Summarization of Clinical Text Using Deep Learning Models

Abstract

This project focused on using clinical text data from the PubMed dataset to train transformer models and deep learning models for text summarization. The primary goal was to develop a system capable of identifying and extracting meaningful information from large clinical texts. Using transformer models and deep learning techniques, the goal was to improve the search for information in the medical literature. The ROUGE score, a widely accepted metric for automated summary assessment, was used to analyze the performance of the trained models. This project involved not only training and optimizing transformer and deep learning models to obtain a comprehensive summary, but also comparing their ROUGE scores to determine which model outperformed the others. This comparative analysis was necessary to determine the most effective model for extracting important insights from clinical texts. The findings have the potential to significantly impact information in the clinical domain, providing researchers and healthcare professionals with faster access to critical information.