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RaKUn: Rank-based Keyword Extraction via Unsupervised Learning and Meta Vertex Aggregation

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Statistical Language and Speech Processing (SLSP 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11816))

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Abstract

Keyword extraction is used for summarizing the content of a document and supports efficient document retrieval, and is as such an indispensable part of modern text-based systems. We explore how load centrality, a graph-theoretic measure applied to graphs derived from a given text can be used to efficiently identify and rank keywords. Introducing meta vertices (aggregates of existing vertices) and systematic redundancy filters, the proposed method performs on par with state-of-the-art for the keyword extraction task on 14 diverse datasets. The proposed method is unsupervised, interpretable and can also be used for document visualization.

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Notes

  1. 1.

    https://github.com/LIAAD/KeywordExtractor-Datasets.

  2. 2.

    We attempted to reproduce YAKE evaluation procedure based on their experimental setup description and also thank the authors for additional explanation regarding the evaluation. For comparison of results we refer to their online repository https://github.com/LIAAD/yake [7].

  3. 3.

    The complete results and the code are available at https://github.com/SkBlaz/rakun.

  4. 4.

    This being a standard procedure, as suggested by the authors of YAKE.

  5. 5.

    https://github.com/LIAAD/yake/blob/master/docs/YAKEvsBaselines.jpg (accessed on: June 11, 2019).

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Acknowledgements

The work was supported by the Slovenian Research Agency through a young researcher grant [BŠ], core research programme (P2-0103), and projects Semantic Data Mining for Linked Open Data (N2-0078) and Terminology and knowledge frames across languages (J6-9372). This work was supported also by the EU Horizon 2020 research and innovation programme, Grant No. 825153, EMBEDDIA (Cross-Lingual Embeddings for Less-Represented Languages in European News Media). The results of this publication reflect only the authors’ views and the EC is not responsible for any use that may be made of the information it contains. We also thank the authors of YAKE for their clarifications.

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Authors and Affiliations

  1. Jožef Stefan International Postgraduate School, Ljubljana, Slovenia

    Blaž Škrlj & Andraž Repar

  2. Jožef Stefan Institute, Ljubljana, Slovenia

    Blaž Škrlj, Andraž Repar & Senja Pollak

  3. Usher Institute, Medical School, University of Edinburgh, Edinburgh, UK

    Senja Pollak

Authors
  1. Blaž Škrlj
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  2. Andraž Repar
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  3. Senja Pollak
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Correspondence to Blaž Škrlj .

Editor information

Editors and Affiliations

  1. Rovira i Virgili University, Tarragona, Spain

    Carlos Martín-Vide

  2. Queen Mary University of London, London, UK

    Matthew Purver

  3. Jožef Stefan Institute, Ljubljana, Slovenia

    Senja Pollak

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Škrlj, B., Repar, A., Pollak, S. (2019). RaKUn: Rank-based Keyword Extraction via Unsupervised Learning and Meta Vertex Aggregation. In: Martín-Vide, C., Purver, M., Pollak, S. (eds) Statistical Language and Speech Processing. SLSP 2019. Lecture Notes in Computer Science(), vol 11816. Springer, Cham. https://doi.org/10.1007/978-3-030-31372-2_26

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  • DOI: https://doi.org/10.1007/978-3-030-31372-2_26

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  • Online ISBN: 978-3-030-31372-2

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