Summary of A Layoutlmv3-based Model For Enhanced Relation Extraction in Visually-rich Documents, by Wiam Adnan et al.
A LayoutLMv3-Based Model for Enhanced Relation Extraction in Visually-Rich Documents
by Wiam Adnan, Joel Tang, Yassine Bel Khayat Zouggari, Seif Edinne Laatiri, Laurent Lam, Fabien Caspani
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper focuses on Relation Extraction (RE) in Visual Document Understanding (VDU), a subfield of Natural Language Processing (NLP). The authors highlight the importance of RE in regrouping entities or obtaining a comprehensive hierarchy of data within a document. They present a model, initialized from LayoutLMv3, that achieves state-of-the-art results on FUNSD and CORD datasets without any specific pre-training and with fewer parameters than existing models. The paper includes an extensive ablation study on FUNSD, demonstrating the significant impact of certain features and modelization choices on performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is about understanding documents that have pictures and other visual elements. Currently, most document understanding research focuses on extracting key information from texts. But what’s missing is studying how entities in a document are related to each other. This paper introduces a new model that can identify these relationships without needing lots of extra training data or complex algorithms. The results show that this model performs well compared to existing models and can even do better with fewer resources. |
Keywords
» Artificial intelligence » Natural language processing » Nlp
