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Combining Machine Learning and Knowledge-Based Systems for Summarizing Interviews

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Foundations of Intelligent Systems (ISMIS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10352))

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Abstract

Achieving optimal results of an automatic summarization process is frequently conditioned by the knowledge of the domain. The performance of general methods is always lower than what can be achieved by introducing custom modifications taking into account the context. Nevertheless, these type of custom adjustments represents a hard work by experts and developers, which is not always possible to achieve due to the high costs. In this work we aim to leverage the features of the documents in order to classify them by using machine learning methods. Once the typology is identified, the application of improvements is done by a knowledge-based system that allows users to easily customize both the summarization process, and the presentation to the final user. The proposed method has been applied with promising results to interviews in a real environment of a major Spanish media group.

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Notes

  1. 1.

    http://www.grupoheraldo.com.

  2. 2.

    http://www.grupoheraldo.com.

  3. 3.

    http://www.heraldo.es.

  4. 4.

    E.g., http://dbpedia.org/page/Category:Spanish-language_surnames.

  5. 5.

    http://swesum.nada.kth.se/index-eng.html.

  6. 6.

    https://www.tools4noobs.com/summarize/.

  7. 7.

    http://autosummarizer.com/.

  8. 8.

    http://textsummarization.net/.

  9. 9.

    ROUGE-L is one of the five evaluation metrics avaliable in ROUGE (a recall-based metric for fixed-length summaries), and it is based on founding the longest common subsequence.

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Acknowledgments

This research work has been supported by the CICYT project TIN2013-46238-C4-4-R, TIN2016-78011-C4-3-R (AEI/FEDER, UE), and DGA/FEDER.

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Authors and Affiliations

  1. IIS Department, University of Zaragoza, Zaragoza, Spain

    Angel Luis Garrido

  2. Computer Department, Grupo Heraldo, Zaragoza, Spain

    Oscar Cardiel, Andrea Aleyxendri & Ruben Quilez

Authors
  1. Angel Luis Garrido
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  2. Oscar Cardiel
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  3. Andrea Aleyxendri
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  4. Ruben Quilez
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Corresponding author

Correspondence to Angel Luis Garrido .

Editor information

Editors and Affiliations

  1. Warsaw University of Technology, Warsaw, Poland

    Marzena Kryszkiewicz

  2. University of Bari Aldo Moro, Bari, Italy

    Annalisa Appice

  3. Institute of Informatics, University of Warsaw, Warsaw, Poland

    Dominik Ślęzak

  4. Faculty of Electronics & Information, Warsaw University of Technology, Warsaw, Poland

    Henryk Rybinski

  5. Institute of Mathematics, Warsaw University, Warsaw, Poland

    Andrzej Skowron

  6. Department of Computer Science, University of North Carolina at Charlotte, North Carolina, USA

    Zbigniew W. Raś

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Garrido, A.L., Cardiel, O., Aleyxendri, A., Quilez, R. (2017). Combining Machine Learning and Knowledge-Based Systems for Summarizing Interviews. In: Kryszkiewicz, M., Appice, A., Ślęzak, D., Rybinski, H., Skowron, A., Raś, Z. (eds) Foundations of Intelligent Systems. ISMIS 2017. Lecture Notes in Computer Science(), vol 10352. Springer, Cham. https://doi.org/10.1007/978-3-319-60438-1_24

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  • DOI: https://doi.org/10.1007/978-3-319-60438-1_24

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