P rompt R ank: Unsupervised Keyphrase Extraction Using Prompt (2023)

Abstract: 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 .

## introduction keyphrase extraction aims to automatically select phrases from a given document that serve as a succinct summary of the main topics, assisting readers in quickly comprehending the key information, and facilitating numerous downstream tasks like information retrieval, text mining, summarization, etc. existing keyphrase extraction work can be divided into two categories: supervised and unsupervised approaches. with the development of deep learning, supervised keyphrase extraction methods have achieved great success by using advanced architectures, such as lstm (alzaidy et al., 2019; * qicheng li is the corresponding author. @xcite and transformer @xcite @xcite . however, supervised methods require large-scale labeled training data and may generalize poorly to new domains. therefore, unsupervised keyphrase extraction methods, mainly including statistics-based @xcite , graph-based @xcite , and embedding-based methods @xcite , are more popular in industry scenarios. recent advancements in embedding-based approaches have led to sota performances that can be further divided into two groups. the first group of methods, such as embedrank @xcite and sifrank @xcite , embed the document and keyphrase candidates into a latent space, calculate the similarity between the embeddings of the document and candidates, then select the top-k most similar keyphrases. due to the discrepancy in length between the document and its candidates, these approaches perform less than optimal and are even worse for long documents. to mitigate such an issue, the second kind of approach is proposed. by leveraging a pre-trained language model (plm), mderank @xcite replaces the candidate's embedding with that of the masked document, in which the candidate is masked from the original document. with the similar length of the masked document and the original document, their distance is measured, and the greater the distance, the more significant the masked candidate as a keyphrase. though mderank solves the problem of length discrepancy, it faces another challenge: plms are not specifically optimized for measuring such distances so contrastive fine-tuning is required to further improve the performance. this places an additional burden on training and deploying keyphrase extraction systems. furthermore, it hinders the rapid adoption of large language models when more powerful plms emerge. inspired by the work clip @xcite , in this paper, we propose to expand the candidate length by putting them into a well-designed template (i.e., prompt). then to compare the document and the corresponding prompts, we adopt the encoder-decoder architecture to map the input (i.e., the original document) and the output (i.e., the prompt) into a shared latent space. the encoderdecoder architecture has been widely adopted and has achieved great success in many fields by aligning the input and output spaces, including machine translation @xcite , image captioning @xcite , etc. our prompt-based unsupervised keyphrase extraction method, dubbed promptrank, can address the aforementioned problems of existing embedding-based approaches simultaneously: on the one hand, the increased length of the prompt can mitigate the discrepancy between the document and the candidate. on the other hand, we can directly leverage plms with an encoder-decoder architecture (e.g., t5 @xcite ) for measuring the similarity without any fine-tuning. specifically, after selecting keyphrase candidates, we feed the given document into the encoder and calculate the probability of generating the candidate with a designed prompt by the decoder. the higher the probability, the more important the candidate. to the best of our knowledge, promptrank is the first to use prompt for unsupervised keyphrase extraction. it only requires the document itself and no more information is needed. exhaustive experiments demonstrate the effectiveness of promptrank on both short and long texts. we believe that our work will encourage more study in this direction. the main contributions of this paper are summarized as follows: prompt learning. in the field of nlp, prompt learning is considered a new paradigm to replace ## promptrank in this section, we introduce the proposed promptrank in detail. the core architecture of our method is shown in figure ## conclusion in this paper, we propose a prompt-based unsupervised keyphrase extraction method, promptrank, using a plm of encoder-decoder architecture. the probability of generating the candidate with a designed prompt by the decoder is calculated to rank candidates. extensive experiments on six widelyused benchmarks demonstrate the effectiveness of our approach, which outperforms strong baselines by a significant margin. we thoroughly examine various factors that influence the performance of promptrank and gain valuable insights. additionally, our method does not require any modification to the architecture of plms and does not introduce any additional parameters, making it a simple yet powerful approach for keyphrase extraction.