Multi-modal supplementary-complementary summarization using multi-objective optimization

Large amounts of multi-modal information online make it difficult for users to obtain proper insights. In this paper, we introduce and formally define the concepts of supplementary and complementary multi-modal summaries in the context of the overlap of information covered by different modalities in the summary output. A new problem statement of combined complementary and supplementary multi-modal summarization (CCS-MMS) is formulated. The problem is then solved in several steps by utilizing the concepts of multi-objective optimization by devising a novel unsupervised framework. An existing multi-modal summarization data set is further extended by adding outputs in different modalities to establish the efficacy of the proposed technique. The results obtained by the proposed approach are compared with several strong baselines; ablation experiments are also conducted to empirically justify the proposed techniques. Furthermore, the proposed model is evaluated separately for different modalities quantitatively and qualitatively, demonstrating the superiority of our approach.