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Multimodal summarization usually suffers from the problem that thecontribution of the visual modality is unclear. Existing multimodalsummarization approaches focus on designing the fusion methods of differentmodalities, while ignoring the adaptive conditions under which visualmodalities are useful. Therefore, we propose a novel Coarse-to-Finecontribution network for multimodal Summarization (CFSum) to consider differentcontributions of images for summarization. First, to eliminate the interferenceof useless images, we propose a pre-filter module to abandon useless images.Second, to make accurate use of useful images, we propose two levels of visualcomplement modules, word level and phrase level. Specifically, imagecontributions are calculated and are adopted to guide the attention of bothtextual and visual modalities. Experimental results have shown that CFSumsignificantly outperforms multiple strong baselines on the standard benchmark.Furthermore, the analysis verifies that useful images can even help generatenon-visual words which are implicitly represented in the image.

CFSum: A Coarse-to-Fine Contribution Network for Multimodal Summarization