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This paper investigates whether EEG signals can complement eye-tracking signals for automatic keyphrase extraction from microblogs. Using the ZuCo corpus, the authors show that cognitive signals, especially EEG, improve AKE performance across different models.
This paper proposes an attention expansion mechanism to enhance keyphrase extraction from long documents by augmenting PLM token representations with out-of-context information, achieving consistent improvements over state-of-the-art models without requiring full-document attention or expensive LLM inference.