Attention Expansion Mechanism Improves Keyphrase Extraction from Long Documents
A team of researchers has introduced an attention expansion mechanism designed to improve keyphrase extraction (KPE) from long documents using pre-trained language models (PLMs). Current PLMs struggle with long documents because relevant keyphrases may be spread across sections that fall outside their limited context windows, while large language models capable of handling longer contexts are computationally expensive. The proposed method offers a practical middle ground by augmenting token representations with information from surrounding out-of-context chunks, consistently improving performance without requiring full-document attention or costly inference.
Pre-trained language models have become a dominant tool for keyphrase extraction, but their fixed context windows make it difficult to capture salient information spread across long documents. Researchers Roberto Martinez Cruz and colleagues propose an attention expansion mechanism that enriches PLM token representations by incorporating signals from surrounding document chunks using pre-trained word embeddings, effectively broadening the model's contextual scope without reprocessing the entire document. The approach was evaluated across five PLM backbones spanning general-purpose, scientific, task-specific, and long-context encoders, tested under two training regimes and on five benchmark corpora from scientific and news domains. Results show consistent improvements in F1 score across all evaluation settings, outperforming state-of-the-art models. Notably, gains were observed even for models already designed for long-context processing, suggesting the mechanism provides genuinely complementary information rather than simply compensating for short context windows. The authors argue this makes attention expansion an efficient and scalable strategy for high-throughput KPE applications where large language model inference is impractical.
What's missing
The paper does not report computational overhead or latency benchmarks for the attention expansion mechanism itself, making it difficult to quantify the efficiency trade-off relative to both standard PLMs and long-context LLMs. Additionally, the study does not address how the method performs on non-English documents or highly domain-specific corpora outside scientific and news genres. As a preprint, the work has not yet undergone formal peer review.
What different sources said
- arXiv cs.AICenter
Attention Expansion: Enhancing Keyphrase Extraction from Long Documents with Attention-Augmented Contextualized Embeddings
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