Attention Expansion: Enhancing Keyphrase Extraction from Long Documents with Attention-Augmented Contextualized Embeddings
Researchers propose an attention expansion mechanism that enhances keyphrase extraction from long documents by augmenting pre-trained language models with information from out-of-context chunks using word embeddings. This approach achieves state-of-the-art performance across multiple benchmark datasets while maintaining computational efficiency compared to full-context LLMs.
This research addresses a fundamental limitation in natural language processing: the inability of pre-trained language models to effectively extract keyphrases from lengthy documents where relevant information spans across sections beyond the model's context window. The attention expansion mechanism represents a pragmatic engineering solution that bridges the gap between the limited context windows of standard PLMs and the computational expense of deploying long-context large language models.
The significance of this work lies in its approach to resource efficiency. Rather than scaling to expensive long-context models, the researchers leverage existing pre-trained word embeddings to augment token representations with information from surrounding chunks. This allows the effective contextual scope to expand without the computational overhead associated with full-document attention mechanisms. The methodology proves robust across diverse evaluation settings, including general-purpose models, scientific domain-specific encoders, and even native long-context models.
For practitioners and developers, this represents a practical advancement in document processing workflows. Organizations requiring high-throughput keyphrase extraction from scientific papers, news articles, or technical documentation can now achieve better performance with existing infrastructure rather than investing in expensive long-context model deployment. The consistent improvements across five different PLM backbones and five benchmark corpora suggest the mechanism provides genuinely complementary information rather than merely compensating for architectural limitations.
Looking forward, this work opens avenues for investigating similar attention augmentation strategies in other NLP tasks constrained by context windows. The efficiency gains demonstrated here could influence how organizations balance model capability with computational cost in production environments.
- →Attention expansion mechanism enhances keyphrase extraction without requiring expensive long-context model inference
- →Approach consistently improves performance across five different pre-trained language model backbones and five benchmark datasets
- →Method leverages pre-trained word embeddings to augment contextualized representations with out-of-context information
- →Results show improvements extend beyond compensating for limited context length to providing genuinely complementary information
- →Technique offers practical efficiency gains for high-throughput document processing in production environments