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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bazrfkan, M., Radmanesh, M.: Using machine learning methods to summarize persian texts. Indian J. Sci. Res. 7(1), 1325–1333 (2014)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Bonifacio, M.: Social innovation: a novel policy stream or a policy compromise? An EU perspective. Eur. Rev. 22(1), 145–169 (2014)
Cheng, J., Lapata, M.: Neural summarization by extracting sentences and words. arXiv preprint arXiv:1603.07252 (2016)
Dong, Y.: A survey on neural network-based summarization methods. arXiv preprint arXiv:1804.04589 (2018)
Fattah, M.A., Ren, F.: GA, MR, FFNN, PNN and GMM based models for automatic text summarization. Comput. Speech Lang. 23(1), 126–144 (2009)
Lin, C.Y.: ROUGE: a package for automatic evaluation of summaries. Text Summarization Branches Out (2004)
Maynard, D., Lepori, B.: Ontologies as bridges between data sources and user queries: the KNOWMAK project experience. In: Proceedings of Science, Technology and Innovation Indicators 2017, STI 2017 (2017)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
Milosevic, N., Gok, A., Nenadic, G.: Classification of intangible social innovation concepts. In: Silberztein, M., Atigui, F., Kornyshova, E., Métais, E., Meziane, F. (eds.) NLDB 2018. LNCS, vol. 10859, pp. 407–418. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91947-8_42
Nallapati, R., Zhai, F., Zhou, B.: SummaRuNNer: a recurrent neural network based sequence model for extractive summarization of documents. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)
Nallapati, R., Zhou, B., Gulcehre, C., Xiang, B., et al.: Abstractive text summarization using sequence-to-sequence RNNs and beyond. arXiv preprint arXiv:1602.06023 (2016)
Nenkova, A., Passonneau, R.: Evaluating content selection in summarization: the pyramid method. In: Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics: HLT-NAACL 2004 (2004)
Neto, J.L., Freitas, A.A., Kaestner, C.A.A.: Automatic text summarization using a machine learning approach. In: Bittencourt, G., Ramalho, G.L. (eds.) SBIA 2002. LNCS (LNAI), vol. 2507, pp. 205–215. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-36127-8_20
Pagliardini, M., Gupta, P., Jaggi, M.: Unsupervised learning of sentence embeddings using compositional n-gram features. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), vol. 1, pp. 528–540 (2018)
Riedhammer, K., Favre, B., Hakkani-Tür, D.: Long story short-global unsupervised models for keyphrase based meeting summarization. Speech Commun. 52(10), 801–815 (2010)
Rush, A.M., Chopra, S., Weston, J.: A neural attention model for abstractive sentence summarization. arXiv preprint arXiv:1509.00685 (2015)
Sarkar, K., Nasipuri, M., Ghose, S.: Using machine learning for medical document summarization. Int. J. Database Theory Appl. 4(1), 31–48 (2011)
Sinha, A., Yadav, A., Gahlot, A.: Extractive text summarization using neural networks. arXiv preprint arXiv:1802.10137 (2018)
Steinberger, J., Ježek, K.: Evaluation measures for text summarization. Comput. Inform. 28(2), 251–275 (2012)
Young, T., Hazarika, D., Poria, S., Cambria, E.: Recent trends in deep learning based natural language processing. IEEE Comput. Intell. Mag. 13(3), 55–75 (2018)
Zhang, Z., Petrak, J., Maynard, D.: Adapted textrank for term extraction: a generic method of improving automatic term extraction algorithms. Procedia Comput. Sci. 137, 102–108 (2018)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-23281-8_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-23280-1
Online ISBN: 978-3-030-23281-8
eBook Packages: Computer ScienceComputer Science (R0)