# sCAKE: Semantic Connectivity Aware Keyword Extraction

arXiv: https://arxiv.org/abs/1811.10831 · HTML: https://mlsanity.com/paper/1811.10831/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2018.

## Built on by (most cited first, 4 of 4)

- [Quantitative Discourse Cohesion Analysis of Scientific Scholarly Texts using Multilayer Networks](https://mlsanity.com/paper/2205.07532/) (2022)
- [Unsupervised extraction of local and global keywords from a single text](https://mlsanity.com/paper/2307.14005/) (2023)
- [BibRank: Automatic Keyphrase Extraction Platform Using~Metadata](https://mlsanity.com/paper/2310.09151/) (2023)
- [Leveraging Human Reading Behavior for Keyphrase Extraction: A Webcam-based Eye-tracking Corpus](https://mlsanity.com/paper/2608.10688/) (2026)

## Abstract (arXiv)

Keyword Extraction is an important task in several text analysis endeavors. In this paper, we present a critical discussion of the issues and challenges ingraph-based keyword extraction methods, along with comprehensive empirical analysis. We propose a parameterless method for constructing graph of text that captures the contextual relation between words. A novel word scoring method is also proposed based on the connection between concepts. We demonstrate that both proposals are individually superior to those followed by the state-of-the-art graph-based keyword extraction algorithms. Combination of the proposed graph construction and scoring methods leads to a novel, parameterless keyword extraction method (sCAKE) based on semantic connectivity of words in the document. Motivated by limited availability of NLP tools for several languages, we also design and present a language-agnostic keyword extraction (LAKE) method. We eliminate the need of NLP tools by using a statistical filter to identify candidate keywords before constructing the graph. We show that the resulting method is a competent solution for extracting keywords from documents oflanguages lacking sophisticated NLP support.