PromptRank：使用提示的无监督关键词抽取
PromptRank: Unsupervised Keyphrase Extraction Using Prompt
AI总结：
提出无监督关键词抽取方法PromptRank，利用预训练语言模型的编码器-解码器架构和提示设计，在六个基准上超越最先进方法，F1分数相对提升最高达34.18%。
摘要：
关键词抽取任务指的是从给定文档中自动选择短语以概括其核心内容。最近，基于嵌入的算法取得了最先进的性能，这些算法根据候选词与文档嵌入的相似度对候选词进行排序。然而，这类解决方案要么难以处理文档与候选词长度差异的问题，要么在未进一步微调的情况下无法充分利用预训练语言模型。为此，在本文中，我们提出了一种简单而有效的无监督方法PromptRank，它基于具有编码器-解码器架构的预训练语言模型。具体来说，PromptRank将文档输入编码器，并通过解码器计算生成带有设计提示的候选词的概率。我们在六个广泛使用的基准上对提出的PromptRank进行了广泛评估。PromptRank优于最先进的方法MDERank，在返回5、10和15个结果时，F1分数相对提高了34.18%、24.87%和17.57%。这证明了使用提示进行无监督关键词抽取的巨大潜力。我们在https://github.com/HLT-NLP/PromptRank上发布了我们的代码。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
