Text summarization is the compression of source text into a condensed version while preserving the information content and overall meaning. Due to the large amount of information research, articles, documents, books, etc., and the development of Internet technologies, text summarization has become an important tool by extracting the most important parts and clarifying the basic purpose of the text. It is very necessary for lawyers and ordinary citizens to conduct thorough research related to their case before answering questions in court. For some time, they had to read very long rulings and try to pick out useful information from them or hire legal editors to create summaries. Due to the lack of research related to summarizing Turkish legal texts, we propose an automated text summarization system by using two novel hybrid pretrained approach models. Our new dataset is collected from official government sites that included Supreme Court, high court and district court cases. We achieved satisfactory results according to the legal professionals (lawyers) evaluation and rouge evaluation metrics, our summary text is Readable and understandable even by non-specialized people. In the future, we aim to obtain better summaries and build an application available to everyone, and open the way for further research using the dataset used in our study.