Attention-Based Graph Neural Network with Global Context Awareness for Document Understanding
Information extraction from documents such as receipts or invoices is a fundamental and crucial step for office automation. Many approaches focus on extracting entities and relationships from plain texts, however, when it comes to document images, such demand becomes quite challenging since visual and layout information are also of great significance to help tackle this problem. In this work, we propose the attention-based graph neural network to combine textual and visual information from document images.Moreover, the global node is introduced in our graph construction algorithm which is used as a virtual hub to collect the information from all the nodes and edges to help improve the performance. Extensive experiments on real-world datasets show that our method outperforms baseline methods by significant margins.
Code (0)
등록된 구현이 없습니다.
Tasks
document understandinggraph constructionGraph Neural NetworkMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Global Context-enhanced Graph Convolutional Networks for Document-level Relation Extraction
Document-level Relation Extraction (RE) is particularly challenging due to complex semantic interactions among multiple entities in a document. Among exiting approaches, Graph Convolutional Networks (GCN) is one of the m…
Document-level Relation ExtractionRelationRelation ExtractionAttentionRetriever: Attention Layers are Secretly Long Document Retrievers
Retrieval augmented generation (RAG) has been widely adopted to help Large Language Models (LLMs) to process tasks involving long documents. However, existing retrieval models are not designed for long document retrieval…
Retinal Vessel Segmentation with Deep Graph and Capsule Reasoning
Effective retinal vessel segmentation requires a sophisticated integration of global contextual awareness and local vessel continuity. To address this challenge, we propose the Graph Capsule Convolution Network (GCC-UNet…
Graph AttentionImage SegmentationMedical Image SegmentationRetinal Vessel Segmentation+2From Global to Local: Learning Context-Aware Graph Representations for Document Classification and Summarization
Recent NLP systems commonly represent documents as linear token sequences. Although this captures sequential order, it can hinder modeling long-range dependencies and global document structure, especially for long texts.…
Document ClassificationDocument SummarizationAttention Instruction: Amplifying Attention in the Middle via Prompting
The context window of large language models has been extended to 128k tokens or more. However, language models still suffer from position bias and have difficulty in accessing and using the middle part of the context due…
PositionQuestion AnsweringRAG