Text Summarization of Medical Documents using Abstractive Techniques

Medical researchers are exposed to enormous amounts of medical information in the form of medical news, clinical trial reports, research articles, etc. Researchers would need the documents' summaries that help them decide to do an in-depth study. Even though there are documents that contain abstracts. a lot of medical documents do not contain abstracts or summaries. The best solution to address this issue is abstractive text summarization. Extracting useful information and summarizing the information from medical documents inclined to the best interests of the user is a challenge that is addressed in this study. For this, several abstractive summarization techniques such as T5(Text-to-Text Transfer Transformer), BART (Bidirectional Auto-Regressive Transformer) and PEGASUS (Pre-training with Extracted Gap-sentences for Abstractive Summarization Sequence-to-sequence) are used and based on ROUGE (Recall-Oriented Understudy for Gisting Evaluation) metrics, PEGASUS is identified to perform better than the other models achieving the highest ROUGE score of 0.37.