RaKUn: Rank-based Keyword extraction via Unsupervised learning and Meta vertex aggregation
Computation and Language
2019-11-12 v3 Information Retrieval
Machine Learning
Abstract
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.
Cite
@article{arxiv.1907.06458,
title = {RaKUn: Rank-based Keyword extraction via Unsupervised learning and Meta vertex aggregation},
author = {Blaž Škrlj and Andraž Repar and Senja Pollak},
journal= {arXiv preprint arXiv:1907.06458},
year = {2019}
}
Comments
The final authenticated publication is available online at https://doi.org/10.1007/978-3-030-31372-2_26