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Cited by 668

The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.

YAKE! Keyword extraction from single documents using multiple local features

Open access
Campos, Ricardo, Mangaravite, Vítor, Pasquali, Arian, Jorge, Alípio, Nunes, Célia, Jatowt, Adam·2020·Information Sciences
DOI 10.1016/j.ins.2019.09.013
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Abstract

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.

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Metadata

Authors
Campos, R., Mangaravite, V., Pasquali, A., Jorge, A., Nunes, C., & Jatowt, A.
Venue
Information Sciences
Publisher
Elsevier BV
Published
2020-01
Volume
509
Pages
257-289
ISSN
0020-0255 (ISSN-L)
DOI
10.1016/j.ins.2019.09.013
OpenAlex
W2973226110
Cited by
668
References
42
FWCI
27.05
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: YAKE! Keyword extraction from single documents using multiple local features. https://app.cochat.ai/papers/10.1016/j.ins.2019.09.013
The paper
Campos, R., Mangaravite, V., Pasquali, A., Jorge, A., Nunes, C., & Jatowt, A. (2020). YAKE! Keyword extraction from single documents using multiple local features. Information Sciences. https://doi.org/10.1016/j.ins.2019.09.013

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.