In recent years, automatic text summarization has witnessed significantadvancement, particularly with the development of transformer-based models.However, the challenge of controlling the readability level of generated summariesremains an under-explored area, especially for languages with complex linguisticfeatures like Turkish. This gap has the effect of impeding effective communicationand also limits the accessibility of information. Controlling readability of textual datais an important element for creating summaries for different audiences with varyingliteracy and education levels, such as students ranging from primary school to graduatelevel, as well as individuals with diverse educational backgrounds. Summaries thatalign with the needs of specific reader groups can improve comprehension andengagement, ensuring that the intended message is effectively communicated. Furthermore, readability adjustment is essential to expand the usability ofsummarization models in educational and professional domains.Current summarization models often don’t have the mechanisms to adjust thecomplexity of their outputs, resulting in summaries that may be too simplistic or overlycomplex for certain types of reader groups. Developing adaptive models that can tailorcontent to specific readability levels is therefore crucial. To address this problem, we create our own custom dataset and train a model with our custom architecture. Our method ensures that readability levels are effectively controlled while maintaining accuracy and coherence. We rigorously compare our model to a supervised fine-tuned baseline, demonstrating its superiority in generating readability-aware summaries.