Leveraging Entity Information for Cross-Modality Correlation Learning: The Entity-Guided Multimodal Summarization
Yanghai Zhang, Ye Liu, Shiwei Wu, Kai Zhang, Xukai Liu, Qi Liu, Enhong Chen · 2024
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 pretrained 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.