Cross-Modal Question Generation: NLP-based Approaches for Text, Image, PDF, and Video Inputs

Natural Language Processing (NLP) has advanced recently, and this has fundamentally changed how humans interact with and understand written material. Among the many uses of natural language processing (NLP), automated question generation (QG) has attracted significant interest in data retrieval and academic research. This study explores novel approaches and tactics for bringing NLP methods to the field of QG. This study provides a thorough synopsis of the many stages of the QG process, including the preparation of preliminary data, the creation of questionnaires, and the interviewing procedure. This study examines each of these phases in detail and discusses about the issues that have surfaced recently along with their associated fixes. It also highlights the critical role that neural networks—in particular, transformer-based models—play in improving QG system performance. Furthermore, this study covers a wide range of applications for automated QG, from search engine building and chatbot interaction to the creation of content for research and education. give case studies and practical applications where the effectiveness of automated QG has been shown. The study also looks at accepted assessment measures and benchmarks for determining the quality of questionnaires that are created. To make it possible to compare QG systems meaningfully, this study emphasizes the value of human participation in research and the usefulness of standardized datasets.