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

arXiv: https://arxiv.org/abs/1907.06458 · HTML: https://mlsanity.com/paper/1907.06458/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2019.

## Built on by (most cited first, 10 of 10)

- [Out of Thin Air: Is Zero-Shot Cross-Lingual Keyword Detection Better Than Unsupervised?](https://mlsanity.com/paper/2202.06650/) (2022)
- [Extending Neural Keyword Extraction with TF-IDF tagset matching](https://mlsanity.com/paper/2102.00472/) (2021)
- [FRAKE: Fusional Real-time Automatic Keyword Extraction](https://mlsanity.com/paper/2104.04830/) (2021)
- [Prioritization of COVID-19-related literature via unsupervised keyphrase extraction and document representation learning](https://mlsanity.com/paper/2110.08874/) (2021)
- [Improving Performance of Automatic Keyword Extraction (AKE) Methods Using PoS-Tagging and Enhanced Semantic-Awareness](https://mlsanity.com/paper/2211.05031/) (2022)
- [Unsupervised extraction of local and global keywords from a single text](https://mlsanity.com/paper/2307.14005/) (2023)
- [Cross-Domain Keyword Extraction with Keyness Patterns](https://mlsanity.com/paper/2409.18724/) (2024)
- [SEKE: Specialised Experts for Keyword Extraction](https://mlsanity.com/paper/2412.14087/) (2024)
- [TT-XAI: Trustworthy Clinical Text Explanations via Keyword Distillation and LLM Reasoning](https://mlsanity.com/paper/2508.08273/) (2025)
- [Letting the Data Speak: Extracting Keywords from Crowdsourced Collections with AI](https://mlsanity.com/paper/2607.09324/) (2026)

## Abstract (arXiv)

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.