Computational argumentation (CA) has emerged, in recent decades, as a powerful formalism for knowl-
edge representation and reasoning in the presence of conflicting information, notably when reasoning
non-monotonically with rules and exceptions. Much existing work in CA has focused, to date, on rea-
soning with given argumentation frameworks (AFs) or, more recently, on using AFs, possibly automat-
ically drawn from other systems, for supporting forms of XAI. In this short paper we focus instead
on the problem of learning AFs from data, with a focus on neuro-symbolic approaches. Specifically,
we overview existing forms of neuro-argumentative (machine) learning, resulting from a combination
of neural machine learning mechanisms and argumentative (symbolic) reasoning. We include in our
overview neuro-symbolic paradigms that integrate reasoners with a natural understanding in argumen-
tative terms, notably those capturing forms of non-monotonic reasoning in logic programming. We also
outline avenues and challenges for future work in this spectrum.