  The keyphrase extraction task refers to the automatic selection of phrases
from a given document to summarize its core content. State-of-the-art (SOTA)
performance has recently been achieved by embedding-based algorithms, which
rank candidates according to how similar their embeddings are to document
embeddings. However, such solutions either struggle with the document and
candidate length discrepancies or fail to fully utilize the pre-trained
language model (PLM) without further fine-tuning. To this end, in this paper,
we propose a simple yet effective unsupervised approach, PromptRank, based on
the PLM with an encoder-decoder architecture. Specifically, PromptRank feeds
the document into the encoder and calculates the probability of generating the
candidate with a designed prompt by the decoder. We extensively evaluate the
proposed PromptRank on six widely used benchmarks. PromptRank outperforms the
SOTA approach MDERank, improving the F1 score relatively by 34.18%, 24.87%, and
17.57% for 5, 10, and 15 returned results, respectively. This demonstrates the
great potential of using prompt for unsupervised keyphrase extraction. We
release our code at https://github.com/HLT-NLP/PromptRank.
