  Modelling musical structure is vital yet challenging for artificial
intelligence systems that generate symbolic music compositions. This literature
review dissects the evolution of techniques for incorporating coherent
structure, from symbolic approaches to foundational and transformative deep
learning methods that harness the power of computation and data across a wide
variety of training paradigms. In the later stages, we review an emerging
technique which we refer to as "sub-task decomposition" that involves
decomposing music generation into separate high-level structural planning and
content creation stages. Such systems incorporate some form of musical
knowledge or neuro-symbolic methods by extracting melodic skeletons or
structural templates to guide the generation. Progress is evident in capturing
motifs and repetitions across all three eras reviewed, yet modelling the
nuanced development of themes across extended compositions in the style of
human composers remains difficult. We outline several key future directions to
realize the synergistic benefits of combining approaches from all eras
examined.
