Research Area:  Machine Learning

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.

Keywords:  
Keyword Extraction
Multiple local features
Unsupervised
Supervised
Machine Learning
Deep Learning

Author(s) Name:  Ricardo Campos, Vítor Mangaravite, Arian Pasquali, Alípio Jorge, Célia Nunes, Adam Jatowt

Publisher name:  Elsevier

DOI:  10.1016/j.ins.2019.09.013

Volume Information:  Volume 509, January 2020, Pages 257-289

Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S0020025519308588