  Speech summarisation techniques take human speech as input and then output an
abridged version as text or speech. Speech summarisation has applications in
many domains from information technology to health care, for example improving
speech archives or reducing clinical documentation burden. This scoping review
maps the speech summarisation literature, with no restrictions on time frame,
language summarised, research method, or paper type. We reviewed a total of 110
papers out of a set of 153 found through a literature search and extracted
speech features used, methods, scope, and training corpora. Most studies employ
one of four speech summarisation architectures: (1) Sentence extraction and
compaction; (2) Feature extraction and classification or rank-based sentence
selection; (3) Sentence compression and compression summarisation; and (4)
Language modelling. We also discuss the strengths and weaknesses of these
different methods and speech features. Overall, supervised methods (e.g. Hidden
Markov support vector machines, Ranking support vector machines, Conditional
random fields) performed better than unsupervised methods. As supervised
methods require manually annotated training data which can be costly, there was
more interest in unsupervised methods. Recent research into unsupervised
methods focusses on extending language modelling, for example by combining
Uni-gram modelling with deep neural networks. Protocol registration: The
protocol for this scoping review is registered at https://osf.io.
