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From Web Crawled Text to Project Descriptions: Automatic Summarizing of Social Innovation Projects

Part of the Lecture Notes in Computer Science book series (LNISA,volume 11608)

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.

Keywords

  • Summarization
  • Evaluation metrics
  • Text mining
  • Natural language processing
  • Social innovation
  • SVM
  • Neural networks

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Fig. 1.

Notes

  1. 1.

    https://gate.ac.uk/projects/knowmak/.

  2. 2.

    https://ec.europa.eu/programmes/horizon2020/en/h2020-section/societal-challenges.

  3. 3.

    http://ec.europa.eu/growth/industry/policy/key-enabling-technologies_en.

  4. 4.

    https://esid.manchester.ac.uk/.

  5. 5.

    https://www.knowmak.eu/.

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Acknowledgements

The work presented in this paper is part of the KNOWMAK project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 726992.

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Correspondence to Nikola Milošević .

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Milošević, N., Marinov, D., Gök, A., Nenadić, G. (2019). From Web Crawled Text to Project Descriptions: Automatic Summarizing of Social Innovation Projects. In: Métais, E., Meziane, F., Vadera, S., Sugumaran, V., Saraee, M. (eds) Natural Language Processing and Information Systems. NLDB 2019. Lecture Notes in Computer Science(), vol 11608. Springer, Cham. https://doi.org/10.1007/978-3-030-23281-8_13

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  • DOI: https://doi.org/10.1007/978-3-030-23281-8_13

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