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.
@article{arxiv.1905.09086,
title = {From web crawled text to project descriptions: automatic summarizing of social innovation projects},
author = {Nikola Milosevic and Dimitar Marinov and Abdullah Gok and Goran Nenadic},
journal= {arXiv preprint arXiv:1905.09086},
year = {2019}
}
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Keywords: Summarization, evaluation metrics, text mining, natural language processing, social innovation, SVM, neural networks Accepted for publication in Proceedings of 24th International Conference on Applications of Natural Language to Information Systems (NLDB2019)