用优化算法同时提升关键词相关性与多样性，避免重复。
Optimizing Keyphrase Ranking for Relevance and Diversity Using Submodular Function Optimization (SFO)
- 将关键词排序建模为可扩展函数最大化问题，兼顾相关性与多样性。
- 在基准数据集上同时优于现有方法的相关性与多样性指标。
- 适合需要高质量关键词提取的摘要生成、信息检索场景。
关键词排序在信息检索和摘要生成中至关重要，能高效索引和提取相关信息。自然语言处理的进步，尤其是大语言模型（LLMs）的应用，提升了关键词抽取与排序效果。然而，传统方法常忽视多样性，导致关键词冗余。本文提出一种基于可扩展函数优化（SFO）的新方法，通过将任务建模为可扩展函数最大化，选择既相关又多样化的关键词。在多个基准数据集上的实验表明，该方法在相关性与多样性指标上均优于现有方法，且执行时间达到最优水平。代码已公开。
原文摘要 · Abstract (English)
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.
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