UC Santa Cruz

Argumentation is an interactive process and frequently occurs in conversations. Social networks and online debates provide a two-way communication platform with a huge amount of opinion and argument rich information. However, the massive amount of information available today is overwhelming to our brain and its ability to efficiently absorb and comprehend. Identifying and integrating arguments across these discussions requires computational methods that can facilitate users to systematically search, analyze and summarize arguments as well as reason about the relationship among arguments. We develop techniques to recognize the specific arguments and counter arguments people tend to advance and group them across discussions as Facets. This entails two sub-tasks: (i) Extract and summarize important arguments, and (ii) Discover similar repeated argument aspects that appear across multiple dialogues on a topic. We present a systematic approach to leverage pyramid based summarization framework to identify central propositions as those arguments that people find most salient, and then rank and select arguments in social media dialogue, which is a novel method for ranking arguments in conversational data. Our results show that adding contextual knowledge from a dialogue improves argument extraction. We introduce a new task of Argument Facet Similarity (AFS), where we develop a new corpus aimed at identifying various facets across opinionated dialogue. A graded argument similarity model was defined that takes as input two sentential arguments and returns a scalar value that categorizes their similarity (AFS). The prediction output obtained from the proposed model improves the results obtained from previous work that defines similarity of short texts as Semantic Textual Similarity (STS).