Argument Mining

Argument mining is a subfield of natural language processing focused on automatically identifying and structuring arguments within text, aiming to understand the relationships between claims, evidence, and other components of an argument. Current research emphasizes developing end-to-end models, often leveraging large language models and deep learning architectures like transformers (e.g., BERT, Longformer), to perform tasks such as stance detection, argument classification, and summarization across diverse text types, including legal documents and debate transcripts. The availability of large, publicly accessible datasets like OpenDebateEvidence is driving progress, and improved argument mining techniques have significant implications for applications ranging from automated debate systems to legal reasoning and improved information analysis.