Attention Expansion: Enhancing Keyphrase Extraction from Long Documents with Attention-Augmented Contextualized Embeddings
Learn how Attention Expansion enhances keyphrase extraction from long documents using attention-augmented contextualized embeddings, improving performance over traditional pre-trained language models.
- Implement Attention Expansion using attention-augmented contextualized embeddings to enhance keyphrase extraction from long documents.
- Use pre-trained language models (PLMs) as a baseline and compare performance with Attention Expansion.
- Experiment with different context window sizes to determine the optimal setting for your specific use case.
- Evaluate the effectiveness of Attention Expansion on a dataset of long documents using metrics such as precision, recall, and F1-score.
- Fine-tune the Attention Expansion model using your specific dataset to further improve performance.
NLP engineers and researchers working on keyphrase extraction tasks can benefit from this technique to improve the accuracy of their models, especially when dealing with long documents.
💡 Attention Expansion enhances keyphrase extraction by augmenting contextualized embeddings with attention mechanisms, allowing for better capture of salient keyphrase evidence across distant document sections.
📚 Improve keyphrase extraction from long docs with Attention Expansion! 🚀
Key Takeaways
Learn how Attention Expansion enhances keyphrase extraction from long documents using attention-augmented contextualized embeddings, improving performance over traditional pre-trained language models.
Full Article
Abstract:
arXiv:2606.10716v1 Announce Type: cross Abstract: 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 across distant document sections that cannot be jointly captured within the limited context window of most PLMs. Although long-context large language models (LLMs) can proces
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