构建分层索引树，实现论文从粗到细的灵活检索。
PaperRegister: Boosting Flexible-grained Paper Search via Hierarchical Register Indexing
- 将论文摘要转为分层索引树，支持细粒度查询。
- 在多种粒度下均达最优性能，细粒度检索效果显著提升。
- 适合需要精准查找模块配置等细节的研究者使用。
随着研究深入，论文检索需求愈发灵活，常需定位具体模块配置等细粒度信息，而传统系统仅基于摘要构建索引，缺乏支撑细粒度检索的细节。为此，本文提出PaperRegister，将传统摘要索引转化为分层索引树，支持灵活粒度的论文检索。在不同粒度的检索任务上实验表明，PaperRegister达到当前最佳性能，尤其在细粒度场景表现突出，展现出在真实应用中解决灵活检索问题的良好潜力。
原文摘要 · Abstract (English)
As researchers delve more deeply into their work, paper search requirements may become more flexible, sometimes involving specific details such as module configuration rather than being limited to coarse-grained topics. However, previous paper search systems are unable to meet these flexible-grained requirements, as previous systems mainly collect paper abstract to construct corpus index, which lacks detailed information to support retrieval by some finer-grained queries. In this work, we propose PaperRegister, which transforms traditional abstract-based index into a hierarchical index tree, thereby supporting queries at flexible granularity. Experiments on paper search tasks across a range of granularity demonstrate that PaperRegister achieves the SOTA performance, and particularly excels in the fine-grained scenarios, highlighting good potential as an effective solution for flexible-grained paper search in real-world applications. https://github.com/Li-Z-Q/PaperRegister.
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