Focused Questions and Answer Generation by Key Content Selection

Automatic Question Answer Generation(AQAG) as a part of Natural Language Processing is an ongoing research trend. AQAG is extremely helpful for Computer-Assisted Assessments where it reduces the expense of manual construction of questions and satisfies the need for a constant supply of new questions. Exam styled questions generated from Automatic Question Generation are mostly "WH"("What", "Who", and "Where") or reading comprehension type. In order for the questions to be most natural or human-like, they need to be diverse or semantically different, based on their levels of assessment, while their answers might remain the same. Hence generating diverse sequences as a part of question generation has become an important NLP task, especially in the education and publishing industry. In this paper, we propose a method of automatically generating answers and diversified sequences corresponding to those answers by introducing a new module called the "Focus Generator". This module guides the decoder in an existing "encoder-decoder" model to generate questions based on selected focus contents. We use a keyword generation algorithm to generate answer tags and a pool of candidate focus from which three best focus are chosen according to the level of information contained in them. We then use this focus content to generate questions that are semantically different from each other. Our work uses a simple architecture by using a single "Focus Generator" module and experimental results show that our module demonstrates 1.2% improvements in BLEU4 score and 20% less training time over the current state-of-the-art model. Our model is also user friendly and provides ease of deriving inference.