RaKUn: Rank-based Keyword extraction via Unsupervised learning and Meta vertex aggregation
This provides an unsupervised and interpretable method for keyword extraction, which is incremental as it builds on existing graph-based approaches.
The paper tackled keyword extraction by using load centrality on text-derived graphs with meta vertices and redundancy filters, achieving performance on par with state-of-the-art methods across 14 diverse datasets.
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.