Multi-Modal Summary Generation using Multi-Objective Optimization

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Abstract

Significant development of communication technology over the past few years has motivated research in multi-modal summarization techniques. A majority of the previous works on multi-modal summarization focus on text and images. In this paper, we propose a novel extractive multi-objective optimization based model to produce a multi-modal summary containing text, images, and videos. Important objectives such as intra-modality salience, cross-modal redundancy and cross-modal similarity are optimized simultaneously in a multi-objective optimization framework to produce effective multi-modal output. The proposed model has been evaluated separately for different modalities, and has been found to perform better than state-of-the-art approaches.

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Jangra, A., Saha, S., Jatowt, A., & Hasanuzzaman, M. (2020). Multi-Modal Summary Generation using Multi-Objective Optimization. In SIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1745–1748). Association for Computing Machinery, Inc. https://doi.org/10.1145/3397271.3401232

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