Multimodal representative answer extraction in community question answering

Abstract

To solve the information overload problem of multimodal answers in community question answering (CQA), this paper proposes a multimodal representative answer extraction method. First, the method of similarity calculation between multimodal answers is constructed, and multimodal clustering is used to cluster answers. Then, a binary multi-objective optimization model with three objective functions including multimodal answer coverage, multimodal answer redundancy, and multimodal answer consistency is constructed to extract a representative subset of answers. The improved Beluga whale optimization algorithm (MTRL-BWO), based on tent mapping, reinforcement learning, and multiple swarm strategy, is designed to increase the diversity of the population while avoiding local optima to improve the search capability and solution accuracy of the algorithm. Experimental results show the feasibility and superior performance of the proposed method.