MUDY: Multi-Granular Dynamic Candidate Contextualization for Unsupervised Keyphrase Extraction

TL;DR

MUDY introduces a novel framework for unsupervised keyphrase extraction that captures multi-granular contextual salience using prompt-based and self-attention scoring methods, outperforming existing approaches.

Contribution

The paper proposes MUDY, a new context-centric method combining prompt-based and self-attention scoring to improve keyphrase extraction by capturing local and global contextual importance.

Findings

MUDY outperforms state-of-the-art baselines on four datasets.

It effectively captures multi-granular contextual salience.

Quantitative and qualitative analyses validate its efficacy.

Abstract

Keyphrase extraction aims to automatically identify concise phrases that effectively represent the content of a document. While recent methods leveraging pre-trained language models (PLMs) have significantly improved the extraction of keyphrases with strong global semantic relevance, they often fall short in capturing the local contextual importance of keyphrases tied to specific subtopics dispersed in a document. In this paper, we propose a novel context-centric framework, MUDY, that effectively captures multi-granular contextual salience of candidate keyphrases. MUDY employs two complementary components: (1) a prompt-based scoring that estimates the generation likelihood of each candidate keyphrase, augmented with candidate-aware weighting to better reflect its local contextual importance, and (2) a self-attention-based scoring that utilizes multi-granular attention patterns from PLMs to assess candidate significance at both the document-wide and segment-specific levels. Evaluations on four real-world datasets demonstrate that MUDY outperforms state-of-the-art baselines in top-k accuracy at various cutoff thresholds. In-depth quantitative and qualitative analyses further highlight the efficacy of context-centric keyphrase extraction with multi-granular saliency. For reproducibility, the source code of MUDY is available at https://github.com/HgKang1/MUDY.