Broadening Labelbased Argumentation Semantics with MayMust Scales. The semantics as to which set of arguments in a given argumentation graph may be acceptable (acceptability semantics) can be characterised in a few different ways. Among them, labellingbased approach allows for concise and flexible determination of acceptability statuses of arguments through assignment of a label indicating acceptance, rejection, or undecided to each argument. In this work, we contemplate a way of broadening it by accommodating may and must conditions for an argument to be accepted or rejected, as determined by the number(s) of rejected and accepted attacking arguments. We show that the broadened labelbased semantics can be used to express more mild indeterminacy than inconsistency for acceptability judgement when, for example, it may be the case that an argument is accepted and when it may also be the case that it is rejected. We identify that finding which conditions a labelling satisfies for every argument can be an undecidable problem, which has an unfavourable implication to semantics. We propose to address this problem by enforcing a labelling to maximally respect the conditions, while keeping the rest that would necessarily cause nontermination labelled undecided.

VIANA: Visual Interactive Annotation of Argumentation Argumentation Mining addresses the challenging tasks of identifying boundaries of argumentative text fragments and extracting their relationships. Fully automated solutions do not reach satisfactory accuracy due to their insufficient incorporation of semantics and domain knowledge. Therefore, experts currently rely on timeconsuming manual annotations. In this paper, we present a visual analytics system that augments the manual annotation process by automatically suggesting which text fragments to annotate next. The accuracy of those suggestions is improved over time by incorporating linguistic knowledge and language modeling to learn a measure of argument similarity from user interactions. Based on a longterm collaboration with domain experts, we identify and model five highlevel analysis tasks. We enable close reading and notetaking, annotation of arguments, argument reconstruction, extraction of argument relations, and exploration of argument graphs. To avoid context switches, we transition between all views through seamless morphing, visually anchoring all text and graphbased layers. We evaluate our system with a twostage expert user study based on a corpus of presidential debates. The results show that experts prefer our system over existing solutions due to the speedup provided by the automatic suggestions and the tight integration between text and graph views.