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Keyphrase Extraction via an Attentive Model

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Digital Libraries: Supporting Open Science (IRCDL 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 988))

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Abstract

Keyphrase extraction is a task of crucial importance for digital libraries. When performing automatically a task of this, the context in which a specific word is located seems to hold a substantial role. To exploit this context, in this paper we propose an architecture based on an Attentive Model: a neural network designed to focus on the most relevant parts of data. A preliminary experimental evaluation on the widely used INSPEC dataset confirms the validity of the approach and shows our approach achieves higher performance than the state of the art.

M. Passon and M. Comuzzo—Equally contributed.

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Acknowledgements

This project was partially supported by the FVG P.O.R. FESR 2014-2020 fund, project “Design of a Digital Assistant based on machine learning and natural language”.

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Authors and Affiliations

  1. Artificial Intelligence Laboratory, University of Udine, Udine, Italy

    Marco Passon, Massimo Comuzzo, Giuseppe Serra & Carlo Tasso

Authors
  1. Marco Passon
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  2. Massimo Comuzzo
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  3. Giuseppe Serra
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  4. Carlo Tasso
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Corresponding authors

Correspondence to Marco Passon , Massimo Comuzzo , Giuseppe Serra or Carlo Tasso .

Editor information

Editors and Affiliations

  1. Italian National Research Council, Pisa, Italy

    Paolo Manghi

  2. Italian National Research Council, Pisa, Italy

    Leonardo Candela

  3. University of Padua, Padua, Italy

    Gianmaria Silvello

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Passon, M., Comuzzo, M., Serra, G., Tasso, C. (2019). Keyphrase Extraction via an Attentive Model. In: Manghi, P., Candela, L., Silvello, G. (eds) Digital Libraries: Supporting Open Science. IRCDL 2019. Communications in Computer and Information Science, vol 988. Springer, Cham. https://doi.org/10.1007/978-3-030-11226-4_24

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  • DOI: https://doi.org/10.1007/978-3-030-11226-4_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-11225-7

  • Online ISBN: 978-3-030-11226-4

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