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Blogs and forums are widely adopted by online communities to debate about various issues. However, a user that wants to cut in on a debate may experience some difficulties in extracting the current accepted positions, and can be discouraged from interacting through these applications. | Combining Textual Entailment and Argumentation Theory for Supporting Online Debates Interactions Elena Cabrio and Serena Villata INRIA 2004 Route des Lucioles BP93 06902 Sophia-Antipolis cedex France. elena.cabrio serena.villata @inria.fr Abstract Blogs and forums are widely adopted by online communities to debate about various issues. However a user that wants to cut in on a debate may experience some difficulties in extracting the current accepted positions and can be discouraged from interacting through these applications. In our paper we combine textual entailment with argumentation theory to automatically extract the arguments from debates and to evaluate their acceptability. 1 Introduction Online debate platforms like Debatepedia1 Twitter2 and many others are becoming more and more popular on the Web. In such applications users are asked to provide their own opinions about selected issues. However it may happen that the debates become rather complicated with several arguments supporting and contradicting each others. Thus it is difficult for potential participants to understand the way the debate is going on i.e. which are the current accepted arguments in a debate. In this paper we propose to support participants of online debates with a framework combining Textual Entailment TE Dagan et al. 2009 and abstract argumentation theory Dung 1995 . In particular TE is adopted to extract the abstract arguments from natural language debates and to provide the relations among these arguments argumentation theory is then used to compute the set of accepted arguments among those obtained from the TE module 1http debatepedia.idebate.org 2http twitter.com 208 i.e. the arguments shared by the majority of the participants without being attacked by other accepted arguments. The originality of the proposed framework lies in the combination of two existing approaches with the goal of supporting participants in their interactions with online debates by automatically detecting .
