# From web crawled text to project descriptions: automatic summarizing of social innovation projects

arXiv: https://arxiv.org/abs/1905.09086 · HTML: https://mlsanity.com/paper/1905.09086/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2019.

## Built on by (most cited first, 1 of 1)

- [Student's t-Distribution: On Measuring the Inter-Rater Reliability When the Observations are Scarce](https://mlsanity.com/paper/2303.04526/) (2023)

## Abstract (arXiv)

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.