Automatic Question Generation System Based Natural Language Processing Using Python

Main Article Content

Selvakani Kandeeban
K.Vasumathi Sundaresan
K. Dinesh Kumar


Natural Language Processing has seen a surge in research on Automatic Question Generation (AQG) in recent times. AQG has proven to be an effective tool for Computer-Assisted Assessments by reducing the costs of manual question construction and generating a continuous stream of new questions. These questions are usually in the format of "WH" or reading comprehension type questions. To ensure natural and diverse questions, they must be semantically distinct based on their assessment level while maintaining consistency in their answers. This is particularly crucial in industries like education and publishing. In our research paper, we introduce a novel approach for generating diverse question sequences and answers using a new module called the "Focus Generator". This module is integrated into an existing "encoder-decoder" model to guide the decoder in generating questions based on selected focus contents. To generate answer tags, we employ a keyword generation algorithm and a pool of candidate focus from which we select the top three based on their level of information. The selected focus content is then utilized to generate semantically distinct questions.


Download data is not yet available.

Article Details

Author Biography

Selvakani Kandeeban, Assistant Professor and Head, PG Department of Computer Science, Government Arts and Science College, Arakkonam - 631051

Respected Editor,Dear Sir/Madam,I am Dr.S.Selvakani, currently working as Head of the Department of Computer Science and Applications, Thiruvalluvar University College of Arts and Science, Arakkonam. Completed Ph.D (Computer Science), M.Tech (Computer Science and Engg), M.Phil (Computer Science), MCA (university III Rank), B.SC (Physics). A dynamic hardworking professional with rich experience of 19 years in Teaching, Administration/ Training & Development. Hands on experience in general administrative activities, Research contributions and Student Counseling. Cambridge University has recognised and awarded certification of "Teachers and Trainers" for remarkable achievement in the field of Teaching. Achievements in various Certificate courses in recent techniques such as Cloud Computing. Result oriented, dynamic, timely accurate reporter. Served as a Reviewer in Various International Journals including SPRINGER (WINE). Served as a Chair person for various Conferences (National and International). Served as a Guest Speaker for Various Symposiums in College as well as School. Member Board of Studies for Various Autonomous Colleges. Served as a Question paper setter in various universities. External Examiner for MCA, M.E., M.Tech., M.Phil from universities and Autonomous Colleges. Published more than 40 papers in the peer reviewed International journals and more than 6 papers in National journals. Besides presented more that 70 papers in the National and International conferences and guiding several PhD scholars in Computer Science.


ChenyangLyu, Liven Shang, Yvette Graham, Jennifer Foster, Xin Jiang, Qun Liu, “Improving Unsupervised Question Answering via Summarization-Informed Question Generationâ€, 2021.

Chidinma A. Nexfor, Ikechukwu E. Onyenwe, “An Automated Multiple-Choice Question Generation Using Natural Language Processing Techniquesâ€,2021.

Jonathan C. Brown, Gwen A. Frishkoff, Maxine Eskenazi. “Automatic Question Generation for Vocabulary Assessmenâ€, 2020.

Ragasudha; M. Saravanan, “Secure Automatic Question Paper with Reconfigurable Constraintsâ€, 2020.

SusmitaGangopadhyay;S.M Ravikiran, “Focused Questions and Answer Generation by Key Content Selectionâ€,2022.