Attention Expansion: Enhancing Keyphrase Extraction from Long Documents with Attention-Augmented Contextualized Embeddings
arXiv:2606.10716v1 Announce Type: new 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 process broader textual contexts, their computational cost limits their practicality for efficient and high-
The continuous development in pre-trained and large language models pushes the boundaries of NLP applications, with advancements seeking to overcome existing computational and context limitations.
Improving keyphrase extraction from long documents is crucial for efficiently processing vast amounts of textual information, impacting research, intelligence gathering, and summarization across many industries.
New methods are making more efficient the processing of extensive text datasets by enabling better contextual understanding for information retrieval and summarization tasks.
- · AI/NLP Researchers
- · Information Retrieval platforms
- · Content summarization services
- · Data analysis firms
- · Manual keyphrase extraction
- · Legacy NLP systems
More accurate and scalable keyphrase extraction from lengthy documents becomes feasible with enhanced attention mechanisms.
This improvement can lead to more efficient and comprehensive knowledge discovery from large text corpora.
The reduced cost and increased accuracy of long-document analysis could accelerate research and development in fields heavily reliant on textual data.
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Read at arXiv cs.CL