Automatic Question Generation System Based Natural Language Processing Using Python

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.


The Focus Generator, Decoder, and Natural Language Processing, Automatic Porker.

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

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

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

Ragasudha; M. Saravanan, “Secure Automatic Question Paper with Reconfigurable Constraints”, 2020.

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



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