Unsupervised extraction of local and global keywords from a single text
This addresses keyword extraction for text analysis, offering a language-independent method applicable to both short and long texts, but it appears incremental as it builds on existing methods like YAKE.
The paper tackles unsupervised keyword extraction from a single text, proposing a method based on word spatial distribution and random permutations, which extracts local and global keywords and uncovers basic themes, with results validated by human annotators showing moderate to substantial agreement.
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.