# PromptRank: Unsupervised Keyphrase Extraction Using Prompt

arXiv: https://arxiv.org/abs/2305.04490 · HTML: https://mlsanity.com/paper/2305.04490/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2023.

## Built on by (most cited first, 13 of 13)

- [Rethinking Model Selection and Decoding for Keyphrase Generation with Pre-trained Sequence-to-Sequence Models](https://mlsanity.com/paper/2310.06374/) (2023)
- [PlainQAFact: Retrieval-augmented Factual Consistency Evaluation Metric for Biomedical Plain Language Summarization](https://mlsanity.com/paper/2503.08890/) (2025)
- [A Preliminary Empirical Study on Prompt-based Unsupervised Keyphrase Extraction](https://mlsanity.com/paper/2405.16571/) (2024)
- [One2set + Large Language Model: Best Partners for Keyphrase Generation](https://mlsanity.com/paper/2410.03421/) (2024)
- [Exploring LLM Capabilities in Extracting DCAT-Compatible Metadata for Data Cataloging](https://mlsanity.com/paper/2507.05282/) (2025)
- [Modeling Contextual Passage Utility for Multihop Question Answering](https://mlsanity.com/paper/2512.06464/) (2025)
- [LongKey: Keyphrase Extraction for Long Documents](https://mlsanity.com/paper/2411.17863/) (2024)
- [ConExion: Concept Extraction with Large Language Models](https://mlsanity.com/paper/2504.12915/) (2025)
- [MAPEX: A Multi-Agent Pipeline for Keyphrase Extraction](https://mlsanity.com/paper/2509.18813/) (2025)
- [Enhancing Unsupervised Keyword Extraction in Academic Papers through Integrating Highlights with Abstract](https://mlsanity.com/paper/2604.19505/) (2026)
- [Attention Expansion: Enhancing Keyphrase Extraction from Long Documents with Attention-Augmented Contextualized Embeddings](https://mlsanity.com/paper/2606.10716/) (2026)
- [Automatic Generation of Highlights for Academic Paper Via Prompt-based Learning](https://mlsanity.com/paper/2606.25253/) (2026)
- [Semantic Networks as Clues: A Theoretical Foundation and Process Optimization for Semantic Network Construction](https://mlsanity.com/paper/2608.01936/) (2026)

## Abstract (arXiv)

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.