Towards More Effective Automatic Question Generation: A Hybrid Approach for Extracting Informative Sentences

Informative Sentence Extraction (ISE) is one of the crucial components in Automatic Question Generation (AQG) and directly influences the quality and relevancy of the generated questions. Instructional texts often contain not only informative but also irrelevant sentences. This results in the creation of poor-quality or distorted questions when irrelevant, non-informative sentences have been used as input. Therefore, the basic problem discussed in this paper is how to provide a systematic method for filtering out such sentences and retaining those that are pedagogically valuable. The purpose of ISE is to filter out irrelevant, low-quality information and retain only the factually dense sentences, express key concepts and are contextually significant. This paper proposes a hybrid approach for extracting informative sentences that combines lexical, statistical, and semantic criteria to identify informative sentences suitable for generating educational questions. The proposed approach consists of two modules: the first module employs four techniques in order to evaluate the informativeness of sentences, which are the keyword-based scoring, Named Entity Recognition (NER), information gain (IG) and Sentence-BERT (SBERT). The second module utilizes multiple fusion strategies to integrate the results derived from the informative sentence extraction techniques. The preprocessed sentences extracted from educational materials were ranked and filtered based on their informativeness coverage. The evaluation results indicate that the hybrid approach can improve the extraction of informative sentences rather than using individual methods. Such a contribution is important for enhancing the performance of downstream tasks in AQG systems, such as distractor generation and question formulation.