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We propose an unsupervised method to extract keywords and keyphrases fromtexts based on a pre-trained language model (LM) and Shannon's informationmaximization. Specifically, our method extracts phrases having the highestconditional entropy under the LM. The resulting set of keyphrases turns out tosolve a relevant information-theoretic problem: if provided as sideinformation, it leads to the expected minimal binary code length in compressingthe text using the LM and an entropy encoder. Alternately, the resulting set isan approximation via a causal LM to the set of phrases that minimize theentropy of the text when conditioned upon it. Empirically, the method providesresults comparable to the most commonly used methods in various keyphraseextraction benchmark challenges.

EntropyRank: Unsupervised Keyphrase Extraction via Side-Information Optimization for Language Model-based Text Compression