Multimodal Sentence Summarization via Multimodal Selective Encoding (2020.coling-main)
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| Challenge: | Existing methods for generating summary from text and image ignore that the image can improve the ability of the encoder to identify highlights of a news event or document. |
| Approach: | They propose a multimodal selective gate network that takes reciprocal relationships between textual and multi-level visual features into account to select highlights of the event. |
| Outcome: | The proposed model can generate summary for a given sentence-image pair using visual signals . it can also capture highlights embedded in the image more accurately, the authors show . |
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VMSMO: Learning to Generate Multimodal Summary for Video-based News Articles (2020.emnlp-main)
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| Challenge: | Multimodal summarization materials lacking a holistic organization by integrating resources from various modalities. |
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| Challenge: | Existing methods to summarize video content have only considered video and image data, and the trend towards multimodal video summarization is changing. |
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| Challenge: | Recent studies on multimodal summarization have shown that the benefits of pre-training and using additional modalities in the input are not orthogonal. |
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| Challenge: | Multimodal Summarization with Multimodal Output (MSMO) is a new approach to produce a multimodal summary that integrates both text and relevant images. |
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| Challenge: | Existing methods for multimodal summarization often inject shallow visual features into deep models, leading to representational mismatches and weak cross-modal grounding. |
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MULSUM: A Multimodal Summarization System with Vis-Aligner and Diversity-Aware Image Selection (2026.eacl-long)
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| Challenge: | Existing systems that condense text and images into concise, faithful digests are inefficient and require large fusion transformers. |
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