Graph-Based Information Extraction for Document Understanding

Document layout understanding is crucial for various applications such as reading comprehension, form filling, and information retrieval. However, existing models struggle to capture complex document structures, leading to suboptimal performance. To address this challenge, the authors propose a novel approach called Graph Neural Networks (GNNs) that incorporate structured information to improve document representation.
The proposed GNN model consists of two main components: node representation and graph neural features. The node representation is based on token embeddings from language models and expresses the text semantics. The node size, on the other hand, reflects the segment’s position in the document, which can convey semantic information about its role.
The GNN model then learns to represent these structured cues using a graph convolutional layer. This allows the model to capture complex relationships between text segments and improve its ability to understand document layout. The authors demonstrate the effectiveness of their approach by achieving a consistent improvement in performance compared to existing models. They also show that incorporating GNNs into existing Transformer-based architectures can significantly enhance their abilities.
In summary, the authors propose a novel approach called Graph Neural Networks (GNNs) to improve document layout understanding by incorporating structured information from text segments. By representing these cues using a graph convolutional layer, GNNs can capture complex relationships between segments and lead to improved performance in various applications.