Summarizing Multi-Party Argumentative Conversations in Reader Comment on News
Emma Barker, Robert Gaizauskas · 2016
Existing approaches to summarizing multi-party argumentative conversations in reader comment are extractive and fail to capture the argumentative nature of these conversations. Work on argument mining proposes schemes for identifying argument elements and relations in text but has not yet addressed how summaries might be generated from a global analysis of a conversation based on these schemes. In this paper we: (1) propose an issue-centred scheme for analysing and graphically representing argument in reader comment discussion in on-line news, and (2) show how summaries capturing the argumentative nature of reader comment can be generated from our graphical representation.