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

Unsupervised extraction of local and global keywords from a single text