Name
Title | ||
|---|---|---|
From web crawled text to project descriptions: automatic summarizing of social innovation projects. |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-23281-8_13 | Lecture Notes in Computer Science |
Keywords | DocType | Volume |
Summarization,Evaluation metrics,Text mining,Natural language processing,Social innovation,SVM,Neural networks | Conference | 11608 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nikola Milosevic | 1 | 31 | 2.38 |
| Dimitar Marinov | 2 | 0 | 0.34 |
| Abdullah Gök | 3 | 0 | 0.34 |
| Goran Nenadic | 4 | 228 | 13.18 |