Breaking Barriers in Keyphrase Extraction with Attention Expansion

Pre-trained language models, or PLMs, have been the go-to for keyphrase extraction (KPE), largely due to their knack for generating rich contextualized representations. But here’s the kicker: long-document KPE is still a beast to tackle. Why? Because key keyphrase evidence is often scattered all over, and most PLMs just can't handle the long context. Enter attention expansion.

The Challenge of Long-Context KPE

Long-context large language models (LLMs) can theoretically process more, but they come with a hefty computational price tag. They're not exactly what you'd call efficient or suitable for high-throughput scenarios. The labs are scrambling to find a balance.

This new attention expansion mechanism seems to hit the sweet spot by augmenting PLM token representations with context from surrounding chunks using pre-trained word embeddings. Basically, it gives you the expanded context without needing to process the whole document or rely on those expensive LLMs.

Why Attention Expansion Stands Out

This approach has been tested across five different PLM backbones. We're talking general-purpose, scientific, task-specific, and long-context encoders. The results? Wild improvements in KPE performance across the board, with notable boosts in F1 scores.

And just like that, the leaderboard shifts. State-of-the-art models are getting a run for their money. The improvements even extend to specialized domain models, showing that this isn’t just a patch job for limited input lengths. It's a real upgrade.

What's Next for Keyphrase Extraction?

This mechanism is a massive step forward. But here's the real twist: Is this the future of KPE? Will we see this attention expansion become the new standard? I'd bet on it. The labs are already looking at how to integrate this into their existing frameworks.

JUST IN: This isn’t just about improving existing models. It's about reshaping how we approach and process long documents entirely. For researchers and techies in the field, it's time to get on board or get left behind.