  In the past decade, social innovation projects have gained the attention of
policy makers, as they address important social issues in an innovative manner.
A database of social innovation is an important source of information that can
expand collaboration between social innovators, drive policy and serve as an
important resource for research. Such a database needs to have projects
described and summarized. In this paper, we propose and compare several methods
(e.g. SVM-based, recurrent neural network based, ensambled) for describing
projects based on the text that is available on project websites. We also
address and propose a new metric for automated evaluation of summaries based on
topic modelling.
