4.7 Article

YAKE! Keyword extraction from single documents using multiple local features

Journal

INFORMATION SCIENCES
Volume 509, Issue -, Pages 257-289

Publisher

ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2019.09.013

Keywords

Keyword extraction; Information extraction; Unsupervised Algorithm

Funding

  1. Project TEC4Growth - Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact - North Portugal Regional Operational Program (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement [NORTE-01-0145-FEDER-000020]
  2. European Regional Development Fund (ERDF) through the Compete 2020 Program [POCI-01-0145-FEDER-006961]
  3. FCT - Fundacao para a Ciencia e a Tecnologia [UID/EEA/50014/2019, UID/MAT/00212/2019]
  4. Fundação para a Ciência e a Tecnologia [UID/MAT/00212/2019] Funding Source: FCT

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As the amount of generated information grows, reading and summarizing texts of large collections turns into a challenging task. Many documents do not come with descriptive terms, thus requiring humans to generate keywords on-the-fly. The need to automate this kind of task demands the development of keyword extraction systems with the ability to automatically identify keywords within the text. One approach is to resort to machine-learning algorithms. These, however, depend on large annotated text corpora, which are not always available. An alternative solution is to consider an unsupervised approach. In this article, we describe YAKE!, a light-weight unsupervised automatic keyword extraction method which rests on statistical text features extracted from single documents to select the most relevant keywords of a text. Our system does not need to be trained on a particular set of documents, nor does it depend on dictionaries, external corpora, text size, language, or domain. To demonstrate the merits and significance of YAKE!, we compare it against ten state-of-the-art unsupervised approaches and one supervised method. Experimental results carried out on top of twenty datasets show that YAKE! significantly outperforms other unsupervised methods on texts of different sizes, languages, and domains. (C) Elsevier Inc. All rights reserved.

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