Papers › Attention-Seeker: Dynamic Self-Attention Scoring for Unsupervised Keyphrase Extraction

Attention-Seeker: Dynamic Self-Attention Scoring for Unsupervised Keyphrase Extraction

17 Sep 2024arXiv:2409.10907archive 2025-07-28

Erwin D. López Z., Cheng Tang, Atsushi Shimada

This paper proposes Attention-Seeker, an unsupervised keyphrase extraction method that leverages self-attention maps from a Large Language Model to estimate the importance of candidate phrases. Our approach identifies specific components - such as layers, heads, and attention vectors - where the model pays significant attention to the key topics of the text. The attention weights provided by these components are then used to score the candidate phrases. Unlike previous models that require manual tuning of parameters (e.g., selection of heads, prompts, hyperparameters), Attention-Seeker dynamically adapts to the input text without any manual adjustments, enhancing its practical applicability. We evaluate Attention-Seeker on four publicly available datasets: Inspec, SemEval2010, SemEval2017, and Krapivin. Our results demonstrate that, even without parameter tuning, Attention-Seeker outperforms most baseline models, achieving state-of-the-art performance on three out of four datasets, particularly excelling in extracting keyphrases from long documents.

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EruM16/Attention-Seeker officialmentioned in paperpytorch report

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Tasks

Keyphrase ExtractionLanguage ModelingLanguage ModellingLarge Language Model

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Keyphrase Extraction Inspec Attention-Seeker F1@10 40.14 #1 of 3 Archive leaderboard report
Keyphrase Extraction Krapivin Attention-Seeker F1@10 18.25 #1 of 2 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

AttentionSoftmax

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