The goal of diversity sampling is to select a representative subset of data in a way that maximizes information contained in the subset while keeping its cardinality small. We introduce the ordered diverse sampling problem based on a new metric that measures the diversity in an ordered list of samples. We present a novel approach for generating ordered diverse samples for textual data that uses principal components on the embedding vectors. The proposed approach is simple and compared with existing approaches using the new metric. We transform standard text classification benchmarks into benchmarks for ordered diverse sampling. Our empirical evaluation shows that prevailing approaches perform 6% to 61% worse than our method while also being more time inefficient. Ablation studies show how the parts of the new approach contribute to the overall metrics.

翻译：多样性采样的目标是以最大化子集所含信息同时保持其基数较小的方式，从数据中选择具有代表性的子集。我们基于一种衡量有序样本列表多样性的新度量指标，提出了有序多样性采样问题。本文提出了一种利用嵌入向量的主成分分析为文本数据生成有序多样性样本的新方法。该方法结构简洁，并基于新度量指标与现有方法进行了对比。我们将标准文本分类基准数据集转化为适用于有序多样性采样的基准测试集。实证评估表明，现有方法的性能比我们的方法低6%至61%，同时时间效率也更低。消融研究揭示了新方法各组成部分对整体指标的贡献机制。