Automated Multiple-Choice Question (MCQ) generation is a rapidly growing field in Natural Language Processing (NLP) that aims to assist educators and trainers in creating high-quality, efficient, and effective assessment materials. This is achieved by analyzing large amounts of textual data, such as educational content, and identifying key concepts and relationships between them. The process of generating MCQs automatically can be broken down into several steps, including text summarization, keyword extraction, distractor generation, and sentence mapping. A new approach is proposed for Text summarization is based on NLP models like XLNet and YAKE is used for keyword extraction. Distractor generation is done using lexical databases such as ConceptNet and WordNet. Sentence mapping is used to identify the main concepts and relationships within a text, which can then be used to formulate questions and options for the multiple-choice questions. The output is a set of MCQs that are semantically related to the input text and can be used for educational and training purposes.