| Challenge: | Existing studies show that multimodal summarization can improve user satisfaction for informativeness of summaries by using information in visual modality. |
| Approach: | They propose a task to generate text and select the most relevant image from the multimodal input and a novel multimodal automatic evaluation method to evaluate multimodal outputs. |
| Outcome: | The proposed method improves user satisfaction by 12.4% compared to the current system . |
Similar Papers
Automatic, Meta and Human Evaluation for Multimodal Summarization with Multimodal Output (2024.naacl-long)
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| Challenge: | Multimodal summarization with multimodal output (MSMO) has attracted increasing research interest . evaluation is an emerging yet underexplored research topic . |
| Approach: | They propose a framework that studies three research questions of MSMO evaluation . they propose an automatic evaluation metric and a meta-evaluation benchmark dataset . |
| Outcome: | The proposed evaluation metric and human-annotated meta-evaluation benchmark are used to assess the quality of evaluation metrics and show the framework is effective. |
Pay More Attention to Images: Numerous Images-Oriented Multimodal Summarization (2025.naacl-long)
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| Challenge: | Existing multimodal summarization approaches struggle with scenarios involving multiple images as input. |
| Approach: | They propose a task to generate multimodal summaries by integrating multiple images as input . they propose 'multimodal information evaluation' method that measures differences between generated summary and input based on multimodal input - and compares various methods . |
| Outcome: | The proposed method correlates more closely with human judgments than five widely used metrics . |
Exploiting Pseudo Image Captions for Multimodal Summarization (2023.findings-acl)
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| Challenge: | Existing approaches to multimodal summarization with multimodal output (MSMO) lack reference images for training, and exposure of image captions during training is inconsistent with MSMO’s task settings. |
| Approach: | They propose a coarse-to-fine image-text alignment mechanism to identify the most relevant sentence of each image in a document, resembling the role of image captions in capturing visual knowledge. |
| Outcome: | The proposed method sets up state-of-the-art on all intermodality and intramodality metrics and improves on image recommendation precision. |
MM-AVS: A Full-Scale Dataset for Multi-modal Summarization (2021.naacl-main)
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| Challenge: | Multimodal summarization materials lacking a holistic organization by integrating resources from various modalities. |
| Approach: | They propose a multimodal article and video summarization dataset that integrates resources from different modalities. |
| Outcome: | The proposed dataset validates the important assistance role of external information for multimodal summarization. |
Leveraging Entity Information for Cross-Modality Correlation Learning: The Entity-Guided Multimodal Summarization (2024.findings-acl)
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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. |
| Approach: | They propose an Entity-Guided Multimodal Summarization model that integrates both text and relevant images to produce a multimodal summary. |
| Outcome: | The proposed model integrates text-image and entity-image information and refines image selection through knowledge distillation from a pre-trained vision-language model. |
Enhancing Large Language Models for Scientific Multimodal Summarization with Multimodal Output (2025.coling-industry)
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| Challenge: | Scientific publications are becoming more multimedia, containing both text and visual content. |
| Approach: | They propose a framework for Scientific Multimodal Summarization with Multimodal Output . it leverages the power of large language models and extends its capability to cross-modal understanding . |
| Outcome: | The proposed framework outperforms uni- and multi-modality methods on two new datasets . it leverages the power of large language models and extends its capability to cross-modal understanding . |
Measuring What Matters Beyond Text: Evaluating Multimodal Summaries by Quality, Alignment, and Diversity (2026.findings-acl)
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| Challenge: | MLLMs have facilitated multimodal summarization with multimodal outputs, but their evaluation is fragmented . MM-Eval integrates assessments of textual quality, cross-modal alignment, and visual diversity . |
| Approach: | They propose a unified evaluation framework that integrates assessments of textual quality, cross-modal alignment, and visual diversity. |
| Outcome: | The proposed framework improves over heuristic aggregation baselines and provides an interpretable, reference-weak framework for comparative evaluation of multimodal summaries. |
Topic-aware Multimodal Summarization (2022.findings-aacl)
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| Challenge: | Existing work on multimodal summarization does not consider the topic of the content. |
| Approach: | They propose a topic-aware MS system which performs two tasks simultaneously: differentiating images into "on-topic" and "off-topic". |
| Outcome: | The proposed system outperforms the state-of-the-art approach by 1.7 % in ROUGE-L metric. |
VMSMO: Learning to Generate Multimodal Summary for Video-based News Articles (2020.emnlp-main)
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| Challenge: | Existing studies show that multimodal news can significantly improve users' sense of satisfaction for informativeness. |
| Approach: | They propose a task of Video-based Multimodal Summarization with Multimodal Output to solve this problem. |
| Outcome: | The proposed method can generate multimodal summaries with a single input . it can model the temporal dependency of video with semantic meaning of article . |
Summary-Oriented Vision Modeling for Multimodal Abstractive Summarization (2023.acl-long)
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| Challenge: | Existing studies on multimodal abstractive summarization focus on how to use extracted visual features to produce a concise summary given the multimodal data. |
| Approach: | They propose to improve the visual quality of the multimodal abstractive summarization model by capturing summary-oriented visual features. |
| Outcome: | The proposed approach achieves state-of-the-art under 44 languages and is highly effective on high-resource English datasets. |