Stretching the Limits: Attention Expansion in Long-Document Keyphrase Extraction

Pre-trained language models (PLMs) have revolutionized keyphrase extraction (KPE) through their ability to generate rich contextualized representations. However, the challenge persists when these models face long documents where key information is scattered beyond their context window's reach. While long-context large language models (LLMs) could theoretically solve this problem, their immense computational cost makes them impractical for high-throughput tasks.

Attention Expansion: A Smarter Approach

Enter the attention expansion mechanism. It enhances PLM token representations with data from surrounding out-of-context chunks using pre-trained word embeddings. This clever strategy expands the effective contextual scope without the need for all-encompassing full-document attention or costly LLM-based inference. The significance? It allows PLMs to perform KPE more efficiently without the heavy computational burden.

We put this mechanism to the test across five PLM backbones, ranging from general-purpose models to those specialized for scientific tasks and long-context encoding. Trials spanned five benchmark corpora from scientific and news domains, all under two distinct training regimes. The results were clear: attention expansion consistently boosts KPE performance, even outperforming state-of-the-art models with noticeable improvements in F1 score.

Why Should We Care?

The improvements are evident across domain-specific, task-specialized, and native long-context models, showing that this mechanism provides complementary, not compensatory, information. This is a major shift. Why rely on resource-heavy LLMs when a refined PLM can do the job just as well, if not better?

It's time we re-evaluate our strategies. In an industry obsessed with bigger models and deeper pockets, the attention expansion method offers a refreshingly efficient alternative. Slapping a model on a GPU rental isn't a convergence thesis, and this innovation proves it.

The Broader Implications

As we move toward increasingly data-driven industries, the demand for efficient KPE grows. The ability to extract key insights from lengthy documents without excessive computational costs opens new doors for AI applications in business intelligence, academia, and beyond. The intersection is real, but ninety percent of the projects aren't. This approach, though, stands out as one that could shift the balance.

So, what does this mean for the future? If the AI can hold a wallet, who writes the risk model? The balance between efficiency and effectiveness in AI-driven processes is more key than ever. With attention expansion, we're inching closer to achieving it.