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.

翻译：关键词抽取任务指从给定文档中自动选取短语以总结其核心内容。近年来，基于嵌入的算法取得了最先进的性能，该类算法通过计算候选短语嵌入与文档嵌入的相似度对候选进行排序。然而，这类方法要么难以处理文档与候选短语的长度差异问题，要么无法在无需微调的情况下充分利用预训练语言模型。为此，本文提出一种简单而有效的无监督方法PromptRank，该方法基于编码器-解码器架构的预训练语言模型。具体而言，PromptRank将文档输入编码器，通过解码器计算在设计的提示下生成候选短语的概率。我们在六个广泛使用的基准数据集上对PromptRank进行了全面评估。结果表明，PromptRank超越了现有最先进方法MDERank，在返回结果数分别为5、10、15时，F1分数相对提升了34.18%、24.87%和17.57%。这充分证明了利用提示进行无监督关键词抽取的巨大潜力。我们已在https://github.com/HLT-NLP/PromptRank 公开代码。