From web crawled text to project descriptions: automatic summarizing of social innovation projects
This work addresses the need for efficient summarization in social innovation databases to support collaboration, policy-making, and research, but it appears incremental as it builds on existing summarization techniques.
The paper tackles the problem of automatically summarizing social innovation projects from web-crawled text, proposing and comparing methods like SVM-based and recurrent neural network approaches, and introduces a new metric for automated evaluation based on topic modeling.
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.