Attention Expansion Mechanism Improves Keyphrase Extraction from Long Documents
Researchers propose an attention expansion mechanism that enhances pre-trained language models' ability to extract keyphrases from long documents by incorporating information from out-of-context sections using word embeddings. The method addresses a key limitation of standard language models: their restricted context windows prevent them from capturing salient information scattered across distant document sections. This approach offers a computationally efficient alternative to expensive long-context large language models while maintaining or improving extraction performance.
A new technique called attention expansion augments pre-trained language models (PLMs) for keyphrase extraction by expanding their effective contextual scope without requiring full-document attention or expensive large language model inference. The mechanism works by enriching token representations with information from surrounding out-of-context chunks using pre-trained word embeddings. Researchers evaluated the approach across five different PLM backbones—including general-purpose, scientific, task-specific, and long-context encoders—using two training regimes and five benchmark corpora from scientific and news domains. Experimental results show consistent performance improvements across all evaluation settings, with notable gains in F1 scores compared to state-of-the-art models. The improvements held even for domain-specialized and native long-context models, suggesting the mechanism provides complementary information rather than simply compensating for input length limitations. This establishes attention expansion as an efficient and practical strategy for handling long-document keyphrase extraction tasks.
What's missing
The paper does not discuss potential limitations of the approach, such as computational overhead of the attention expansion mechanism itself, failure cases, or scenarios where the method may underperform compared to baselines.
What different sources said
- arXiv cs.AICenter
Attention Expansion: Enhancing Keyphrase Extraction from Long Documents with Attention-Augmented Contextualized Embeddings
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