Papers with Multimodal Document-level Triple Extraction

1 papers
Multimodal Document-level Triple Extraction via Dynamic Graph Enhancement and Relation-Aware Reflection (2025.findings-emnlp)

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Challenge: Existing methods for extracting structured triples knowledge from multimodal documents face limitations in simultaneously processing long textual content and multiple associated images for triple extraction.
Approach: They propose a multimodal document-level triple extraction framework that integrates multimodal text and visual content into a large language model and injects the global information and external knowledge into the model.
Outcome: The proposed framework outperforms the state-of-the-art methods and fills the gap in multimodal document extraction.

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