Sentence Entity Graph

Sentence entity graphs represent sentences as graphs where nodes are entities and sentences, and edges capture relationships between them. Research focuses on leveraging these graphs, often in conjunction with graph neural networks (GNNs) and attention mechanisms, to improve tasks like relation extraction, text summarization, and sentence ordering. These methods aim to better capture contextual information and inter-sentence dependencies, leading to more accurate and robust NLP models. The resulting advancements have significant implications for various NLP applications, particularly those involving long documents or complex relationships between entities.