Zahra Tavallaei - Masters student, Artificial intelligence and robotics, Department of computer engineering, Apadana Institute of Higher Education, Shiraz, Iran.
Haleh Homayouni - Assistant professor, Faculty member, Department of computer engineering, Apadana Institute of Higher Education, Shiraz, Iran.

This article focuses on the summarization of medical documents using extractive and abstractive approaches with the use of natural language processing. In this method, key and conceptual information is extracted from medical texts to generate useful and concise summaries. The utilized medical dataset, text extraction and abstraction methods, existing challenges and issues, evaluation of results, and suggestions for improvement in this field are examined. The results indicate that the use of extractive and abstractive methods leads to a significant improvement in the quality and effectiveness of the summaries. Abstract method uses the original text and in the abstract method a summary of the perceived text is presented. In this article, we examine the techniques used in each method to find the most optimal solution and facilitating access to valuable medical information. The text summary should include all the important points and essential information of the original text. So with this principle, medical records are summed up.

summarization, medical documents, information extraction, natural language processing