  Automatic text summarisation has drawn considerable interest in the area of
software engineering. It is challenging to summarise the activities related to
a software project, (1) because of the volume and heterogeneity of involved
software artefacts, and (2) because it is unclear what information a developer
seeks in such a multi-document summary. We present the first framework for
summarising multi-document software artefacts containing heterogeneous data
within a given time frame. To produce human-like summaries, we employ a range
of iterative heuristics to minimise the cosine-similarity between texts and
high-dimensional feature vectors. A first study shows that users find the
automatically generated summaries the most useful when they are generated using
word similarity and based on the eight most relevant software artefacts.
