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

## TL;DR

This paper explores various automated methods, including machine learning and neural networks, to generate summaries of social innovation projects from web texts, aiming to enhance database quality and research utility.

## Contribution

It introduces and compares multiple summarization techniques and proposes a novel metric for evaluating summaries based on topic modeling.

## Key findings

- Recurrent neural network methods outperform traditional models.
- Ensemble approaches improve summary quality.
- A new topic-based evaluation metric is effective.

## 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.