Papers › PromptRank: Unsupervised Keyphrase Extraction Using Prompt

PromptRank: Unsupervised Keyphrase Extraction Using Prompt

8 May 2023arXiv:2305.04490archive 2025-07-28

Aobo Kong, Shiwan Zhao, Hao Chen, Qicheng Li, Yong Qin, Ruiqi Sun, Xiaoyan Bai

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.

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hlt-nlp/promptrank officialmentioned in papermentioned on GitHubpytorch report
nku-hlt/promptrank mentioned on GitHubpytorch report

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Tasks

DecoderKeyphrase ExtractionLanguage ModelingLanguage Modelling

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Keyphrase Extraction Inspec PromptRank F1@10 37.88 #2 of 3 Archive leaderboard report
Keyphrase Extraction Krapivin PromptRank F1@10 16.71 #2 of 2 Archive leaderboard report
Keyphrase Extraction NUS PromptRank F1@10 20.13 #1 of 1 Archive leaderboard report

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