Attention expansion boosts keyphrase extraction from long documents

By PulseAugur Editorial · [1 sources] · 2026-06-09 11:24

Researchers have developed an "attention expansion" mechanism to improve keyphrase extraction from long documents. This method augments pre-trained language model representations with information from surrounding text chunks, effectively broadening the model's contextual scope without needing full-document attention or costly large language model inference. Experiments across various models and datasets show consistent performance gains, establishing attention expansion as an efficient strategy for long-document keyphrase extraction. AI

IMPACT Enhances efficiency for NLP tasks involving long texts, potentially improving information retrieval and summarization.

RANK_REASON Academic paper introducing a new method for keyphrase extraction. [lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · José Portela · 2026-06-09 11:24

Attention Expansion: Enhancing Keyphrase Extraction from Long Documents with Attention-Augmented Contextualized Embeddings

Pre-trained language models (PLMs) have achieved strong performance in keyphrase extraction (KPE), largely due to their ability to generate rich contextualized representations. However, long-document KPE remains challenging because salient keyphrase evidence may be scattered acro…

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Attention Expansion: Enhancing Keyphrase Extraction from Long Documents with Attention-Augmented Contextualized Embeddings

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