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AI Research
arXiv CS.AI·Jun 10·5 min read
Attention Expansion: Enhancing Keyphrase Extraction from Long Documents with Attention-Augmented Contextualized Embeddings
TL;DR
Keyphrase extraction (KPE) from long documents is challenging due to the limited context window of pre-trained language models (PLMs). An attention expansion mechanism was developed to enhance PLM token representations by incorporating information from surrounding text chunks.
✦ Why It Matters
Engineers can implement attention expansion to improve keyphrase extraction in long documents without high computational costs.
Key Takeaways
- Integrate Attention Expansion for better KPE performance.
- Utilize pre-trained word embeddings to enhance context.
- Test across various PLM architectures for optimal results.
- Focus on long-document processing for improved efficiency.
How It Works
Attention Expansion enhances PLM token representations by incorporating information from surrounding out-of-context text chunks. This is achieved using pre-trained word embeddings, which allows the model to capture salient keyphrase evidence that may be distributed across distant sections of a long document.