Towards Topic to Question Generation

Abstract

This paper is concerned with automatic generation of all possible questions from a topic of interest. Specifically, we consider that each topic is associated with a body of texts containing useful information about the topic. Then, questions are generated by exploiting the named entity information and the predicate argument structures of the sentences present in the body of texts. The importance of the generated questions is measured using Latent Dirichlet Allocation (LDA) by identifying the sub-topics (which are closely related to the original topic) in the given body of texts and applying the Extended String Subsequence Kernel (ESSK) to calculate their similarity with the questions. We also propose the use of syntactic tree kernels for the automatic judgement of the syntactic correctness of the questions. The questions are ranked by both considering their importance (in the context of the given body of texts) and syntactic correctness. To the best of our knowledge, no other study has accomplished this task in our setting before. A series of experiments demonstrate that the proposed topic to question generation approach can significantly outperform the state-of-the-art results.