BART Fine Tuning based Abstractive Summarization of Patients Medical Questions Texts

Today, many people get counseling about their problems by interviewing doctors or communicating via online platforms. With neural architectures such as Transformer achieving high-performance results in the field of natural language processing, the use of these approaches has become quite common in solving many natural language processing problems in the medical field. In this study, a method using BART (Bidirectional Auto-Regressive Transformer) neural architecture is proposed for abstractive summarization of questions asked by patients to doctors. In the proposed method, the pretrained BART neural architecture is retrained using a dataset consisting of questions asked by patients to doctors and summaries of these questions. The evaluation of the summary questions obtained was carried out with the ROUGE metric and compared with other approaches in the literature from different perspectives. When the comparative results are examined, the ROUGE performance of our approach is higher than 92% of other studies that use abstractive medical summary.