Modeling Paragraph-Level Vision-Language Semantic Alignment for Multi-Modal Summarization

• URL: http://arxiv.org/abs/2208.11303v3
• Date: Wed, 10 May 2023 15:54:12 GMT
• Title: Modeling Paragraph-Level Vision-Language Semantic Alignment for Multi-Modal Summarization
• Authors: Chenhao Cui, Xinnian Liang, Shuangzhi Wu, Zhoujun Li
• Abstract summary: We propose ViL-Sum to jointly model paragraph-level textbfVision-textbfLanguage Semantic Alignment and Multi-Modal textbfSummarization. The core of ViL-Sum is a joint multi-modal encoder with two well-designed tasks, image reordering and image selection. Experimental results show that our proposed ViL-Sum significantly outperforms current state-of-the-art methods.
• Score: 23.475411831792716
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Most current multi-modal summarization methods follow a cascaded manner, where an off-the-shelf object detector is first used to extract visual features, then these features are fused with language representations to generate the summary with an encoder-decoder model. The cascaded way cannot capture the semantic alignments between images and paragraphs, which are crucial to a precise summary. In this paper, we propose ViL-Sum to jointly model paragraph-level \textbf{Vi}sion-\textbf{L}anguage Semantic Alignment and Multi-Modal \textbf{Sum}marization. The core of ViL-Sum is a joint multi-modal encoder with two well-designed tasks, image reordering and image selection. The joint multi-modal encoder captures the interactions between modalities, where the reordering task guides the model to learn paragraph-level semantic alignment and the selection task guides the model to selected summary-related images in the final summary. Experimental results show that our proposed ViL-Sum significantly outperforms current state-of-the-art methods. In further analysis, we find that two well-designed tasks and joint multi-modal encoder can effectively guide the model to learn reasonable paragraphs-images and summary-images relations.

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