Optimizing Keyphrase Ranking for Relevance and Diversity Using Submodular Function Optimization (SFO)
This addresses the issue of diversity in keyphrase ranking for information retrieval and summarization, though it appears incremental as it builds on existing methods with a novel optimization approach.
The paper tackled the problem of redundant keyphrases in keyphrase ranking by proposing a method using Submodular Function Optimization (SFO) to balance relevance and diversity, achieving state-of-the-art performance in both metrics and execution time on benchmark datasets.
Keyphrase ranking plays a crucial role in information retrieval and summarization by indexing and retrieving relevant information efficiently. Advances in natural language processing, especially large language models (LLMs), have improved keyphrase extraction and ranking. However, traditional methods often overlook diversity, resulting in redundant keyphrases. We propose a novel approach using Submodular Function Optimization (SFO) to balance relevance and diversity in keyphrase ranking. By framing the task as submodular maximization, our method selects diverse and representative keyphrases. Experiments on benchmark datasets show that our approach outperforms existing methods in both relevance and diversity metrics, achieving SOTA performance in execution time. Our code is available online.