From web crawled text to project descriptions: automatic summarizing of social innovation projects
Nikola Milosevic, Dimitar Marinov, Abdullah Gok, Goran Nenadic
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.
cs.CL, cs.IR, cs.LG