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

## TL;DR

RaKUn is an unsupervised, graph-based keyword extraction method that uses load centrality and meta vertices to effectively identify and rank keywords across diverse datasets, supporting document summarization and visualization.

## Contribution

The paper introduces a novel unsupervised keyword extraction approach using load centrality and meta vertices, achieving competitive performance with interpretability.

## Key findings

- Performs on par with state-of-the-art methods across 14 datasets
- Uses load centrality for effective keyword ranking
- Supports document visualization

## 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.