  Automatic multiple-choice question generation (MCQG) is a useful yet
challenging task in Natural Language Processing (NLP). It is the task of
automatic generation of correct and relevant questions from textual data.
Despite its usefulness, manually creating sizeable, meaningful and relevant
questions is a time-consuming and challenging task for teachers. In this paper,
we present an NLP-based system for automatic MCQG for Computer-Based Testing
Examination (CBTE).We used NLP technique to extract keywords that are important
words in a given lesson material. To validate that the system is not perverse,
five lesson materials were used to check the effectiveness and efficiency of
the system. The manually extracted keywords by the teacher were compared to the
auto-generated keywords and the result shows that the system was capable of
extracting keywords from lesson materials in setting examinable questions. This
outcome is presented in a user-friendly interface for easy accessibility.
