Towards Visually Grounded Multimodal Summarization via Cross-Modal Transformer and Gated Attention

Multimodal summarization requires models to jointly understand textual and visual inputs to generate concise, semantically coherent summaries. Existing methods often inject shallow visual features into deep language models, leading to representational mismatches and weak cross-modal grounding. We propose a unified framework that jointly performs text summarization and representative image selection. Our system, SPeCTrA-Sum (Sampler Perceiver with Cross-modal Transformer and gated Attention for S

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ethics.ai (12 May 2026), “Towards Visually Grounded Multimodal Summarization via Cross-Modal Transformer and Gated Attention,” evidence record 4488, https://ethics.ai/record/4488 (originally published by arXiv).

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