# Unsupervised extraction of local and global keywords from a single text

arXiv: https://arxiv.org/abs/2307.14005 · HTML: https://mlsanity.com/paper/2307.14005/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 79 references that are in the map.
- First posted on arXiv 26 Jul 2023.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Review of Keyphrase Extraction](https://mlsanity.com/paper/1905.05044/) (2019)
- [PatternRank: Leveraging Pretrained Language Models and Part of Speech for Unsupervised Keyphrase Extraction](https://mlsanity.com/paper/2210.05245/) (2022)
- [Unsupervised Keyphrase Extraction by Jointly Modeling Local and Global Context](https://mlsanity.com/paper/2109.07293/) (2021)
- [TNT-KID: Transformer-based Neural Tagger for Keyword Identification](https://mlsanity.com/paper/2003.09166/) (2020)
- [RaKUn: Rank-based Keyword extraction via Unsupervised learning and Meta vertex aggregation](https://mlsanity.com/paper/1907.06458/) (2019)
- [sCAKE: Semantic Connectivity Aware Keyword Extraction](https://mlsanity.com/paper/1811.10831/) (2018)
- [Two halves of a meaningful text are statistically different](https://mlsanity.com/paper/2004.06474/) (2020)
- [Explaining Zipf's Law via Mental Lexicon](https://mlsanity.com/paper/1302.4383/) (2013)
- [Optimal alphabet for single text compression](https://mlsanity.com/paper/2201.05234/) (2022)

## Abstract (arXiv)

We propose an unsupervised, corpus-independent method to extract keywords from a single text. It is based on the spatial distribution of words and the response of this distribution to a random permutation of words. As compared to existing methods (such as e.g. YAKE) our method has three advantages. First, it is significantly more effective at extracting keywords from long texts. Second, it allows inference of two types of keywords: local and global. Third, it uncovers basic themes in texts. Additionally, our method is language-independent and applies to short texts. The results are obtained via human annotators with previous knowledge of texts from our database of classical literary works (the agreement between annotators is from moderate to substantial). Our results are supported via human-independent arguments based on the average length of extracted content words and on the average number of nouns in extracted words. We discuss relations of keywords with higher-order textual features and reveal a connection between keywords and chapter divisions.