Leveraging Entity Information for Cross-Modality Correlation Learning: The Entity-Guided Multimodal Summarization

• URL: http://arxiv.org/abs/2408.03149v1
• Date: Tue, 6 Aug 2024 12:45:56 GMT
• Title: Leveraging Entity Information for Cross-Modality Correlation Learning: The Entity-Guided Multimodal Summarization
• Authors: Yanghai Zhang, Ye Liu, Shiwei Wu, Kai Zhang, Xukai Liu, Qi Liu, Enhong Chen,
• Abstract summary: Multimodal Summarization with Multimodal Output (MSMO) aims to produce a multimodal summary that integrates both text and relevant images. In this paper, we propose an Entity-Guided Multimodal Summarization model (EGMS) Our model, building on BART, utilizes dual multimodal encoders with shared weights to process text-image and entity-image information concurrently.
• Score: 49.08348604716746
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid increase in multimedia data has spurred advancements in Multimodal Summarization with Multimodal Output (MSMO), which aims to produce a multimodal summary that integrates both text and relevant images. The inherent heterogeneity of content within multimodal inputs and outputs presents a significant challenge to the execution of MSMO. Traditional approaches typically adopt a holistic perspective on coarse image-text data or individual visual objects, overlooking the essential connections between objects and the entities they represent. To integrate the fine-grained entity knowledge, we propose an Entity-Guided Multimodal Summarization model (EGMS). Our model, building on BART, utilizes dual multimodal encoders with shared weights to process text-image and entity-image information concurrently. A gating mechanism then combines visual data for enhanced textual summary generation, while image selection is refined through knowledge distillation from a pre-trained vision-language model. Extensive experiments on public MSMO dataset validate the superiority of the EGMS method, which also prove the necessity to incorporate entity information into MSMO problem.

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