This study investigates retrieval-augmented summarization by specifically examining the impact of exemplar summary lengths under length constraints, not covered by previous work. We propose a Diverse Length-aware Maximal Marginal Relevance (DL-MMR) algorithm to better control summary lengths. This algorithm combines the query relevance with diverse target lengths in retrieval-augmented summarization. Unlike previous methods that necessitate exhaustive exemplar exemplar relevance comparisons using MMR, DL-MMR considers the exemplar target length as well and avoids comparing exemplars to each other, thereby reducing computational cost and conserving memory during the construction of an exemplar pool. Experimental results showed the effectiveness of DL-MMR, which considers length diversity, compared to the original MMR algorithm. DL-MMR additionally showed the effectiveness in memory saving of 781,513 times and computational cost reduction of 500,092 times, while maintaining the same level of informativeness.

本研究针对检索增强摘要中的长度限制问题进行了探讨，特别是以往研究未涉及的示例摘要长度的影响。我们提出了一种新的算法Diverse Length-aware Maximal Marginal Relevance（DL-MMR），该算法在检索增强摘要中合理控制摘要长度，显示出显著的计算效率和内存节省效果。

考虑检索增强摘要中的长度多样性