  This paper presents the results of research on supervised extractive text
summarisation for scientific articles. We show that a simple sequential tagging
model based only on the text within a document achieves high results against a
simple classification model. Improvements can be achieved through additional
sentence-level features, though these were minimal. Through further analysis,
we show the potential of the sequential model relying on the structure of the
document depending on the academic discipline which the document is from.
