RaKUn: Rank-based Keyword extraction via Unsupervised learning and Meta vertex aggregation

Autor:	Škrlj, Blaž, Repar, Andraž, Pollak, Senja
Rok vydání:	2019
Předmět:	Computer Science - Computation and Language Computer Science - Information Retrieval Computer Science - Machine Learning
Zdroj:	Statistical Language and Speech Processing 2019 Proceedings
Druh dokumentu:	Working Paper
DOI:	10.1007/978-3-030-31372-2_26
Popis:	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. Comment: The final authenticated publication is available online at https://doi.org/10.1007/978-3-030-31372-2_26
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1907.06458 Zobrazit plný text záznamu View this record from Arxiv