Hierarchical Discourse

Hierarchical discourse analysis focuses on understanding the structural organization of text beyond individual sentences, aiming to model the relationships between clauses, sentences, and paragraphs to better represent meaning. Current research emphasizes incorporating hierarchical discourse features into various natural language processing tasks, leveraging models like BiLSTMs and prompt-tuning techniques to improve performance in areas such as headline generation, coreference resolution, and fake news detection. This work is significant because accurately capturing hierarchical discourse structure enhances the ability of machines to understand complex texts, leading to improved performance in applications ranging from text summarization and information retrieval to the detection of machine-generated content.