YAKE! Keyword extraction from single documents using multiple local features

By Ricardo Campos, Vítor Mangaravite, Arian Pasquali, Alípio Mário Jorge, C. Nunes, Adam Jatowt. Information Sciences, 2020

714 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/9cb32bdd43f64b36cb447ba1307869c5d8bf675c

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Citations in the corpus

Constrained Abstractive Summarization: Preserving Factual Consistency with Constrained Generation (2020, methodology): “In preliminary experiments, we find commonly used unsupervised KPE methods (Campos et al., 2020) insufficient to provide high-quality constraints.”
Human-Machine Collaboration Approaches to Build a Dialogue Dataset for Hate Speech Countering (2022, methodology): “We employ the YAKE keyword extractor (Campos et al., 2020) to extract two keywords from each HS and CN of PAIRS gold and perform a concatenation similar to the previous strategies.”
Rule-based Morphological Inflection Improves Neural Terminology Translation (2021, methodology): “We extract keyphrases from each source sentence using Yet Another Keyword Extractor (YAKE) (Campos et al., 2020)11 and filter out phrases with high or medium frequency in the training corpora since they are mostly common and domain-generic phrases.”
Help me write a Poem - Instruction Tuning as a Vehicle for Collaborative Poetry Writing (2022, methodology): “Hence we use YAKE (Campos et al., 2018, 2020), an unsupervised automatic keyword extraction method for selecting salient words from the Haiku that serves as its title.”