Query-focused summarization (QFS) aims to produce summaries that answer particular questions of interest, enabling greater user control and personalization. With the advent of large language models (LLMs), shows their impressive capability of textual understanding through large-scale pretraining, which implies the great potential of extractive snippet generation. In this paper, we systematically investigated two indispensable characteristics that the LLMs-based QFS models should be harnessed, Lengthy Document Summarization and Efficiently Fine-grained Query-LLM Alignment, respectively. Correspondingly, we propose two modules called Query-aware HyperExpert and Query-focused Infini-attention to access the aforementioned characteristics. These innovations pave the way for broader application and accessibility in the field of QFS technology. Extensive experiments conducted on existing QFS benchmarks indicate the effectiveness and generalizability of the proposed approach. Our code is publicly available at https://github.com/DCDmllm/IDEAL_Summary.

翻译：查询聚焦式摘要旨在生成能够回答特定关注问题的摘要，从而增强用户控制与个性化程度。随着大语言模型的出现，其通过大规模预训练展现出的卓越文本理解能力，暗示了其在抽取式片段生成方面的巨大潜力。本文系统研究了大语言模型驱动的查询聚焦式摘要模型应具备的两项关键特性：长文档摘要能力与高效细粒度查询-大语言模型对齐机制。相应地，我们提出了查询感知超专家模块与查询聚焦无限注意力模块来实现上述特性。这些创新为查询聚焦式摘要技术更广泛的应用与普及铺平了道路。在现有查询聚焦式摘要基准测试上进行的大量实验证明了所提方法的有效性与泛化能力。我们的代码已公开于https://github.com/DCDmllm/IDEAL_Summary。