  The recent evolution in Natural Language Processing (NLP) methods, in
particular in the field of argumentation mining, has the potential to transform
the way we interact with text, supporting the interpretation and analysis of
complex discourse and debates. Can a graphic visualisation of complex
argumentation enable a more critical interpretation of the arguments? This
study focuses on analysing the impact of argumentation graphs (AGs) compared
with regular texts for supporting argument interpretation. We found that AGs
outperformed the extrinsic metrics throughout most UEQ scales as well as the
NASA-TLX workload in all the terms but not in temporal or physical demand. The
AG model was liked by a more significant number of participants, despite the
fact that both the text-based and AG models yielded comparable outcomes in the
critical interpretation in terms of working memory and altering participants
decisions. The interpretation process involves reference to argumentation
schemes (linked to critical questions (CQs)) in AGs. Interestingly, we found
that the participants chose more CQs (using argument schemes in AGs) when they
were less familiar with the argument topics, making AG schemes on some scales
(relatively) supportive of the interpretation process. Therefore, AGs were
considered to deliver a more critical approach to argument interpretation,
especially with unfamiliar topics. Based on the 25 participants conducted in
this study, it appears that AG has demonstrated an overall positive effect on
the argument interpretation process.
