Discourse Aware Graph Network

Discourse-aware graph networks (DAGNs) aim to improve natural language processing by explicitly modeling the relationships between sentences and utterances within a text, going beyond simple word-level analysis. Current research focuses on applying DAGNs to tasks like meeting summarization and question answering, often using graph neural networks (GNNs) to learn representations from these discourse graphs, sometimes incorporating linguistic theories like Combinatory Categorial Grammar (CCG). This approach enhances the understanding of complex textual structures, leading to improved performance in various NLP tasks and offering valuable insights into the dynamics of political discourse and other multifaceted debates.