PaperRegister: Boosting Flexible-grained Paper Search via Hierarchical
  Register Indexing
Paper search is an important activity for researchers, typically involving
using a query with description of a topic to find relevant papers. As research
deepens, 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 these systems mainly
collect paper abstracts to construct index of corpus, which lack detailed
information to support retrieval by finer-grained queries. In this work, we
propose PaperRegister, consisted of offline hierarchical indexing and online
adaptive retrieval, transforming traditional abstract-based index into
hierarchical index tree for paper search, thereby supporting queries at
flexible granularity. Experiments on paper search tasks across a range of
granularity demonstrate that PaperRegister achieves the state-of-the-art
performance, and particularly excels in fine-grained scenarios, highlighting
the good potential as an effective solution for flexible-grained paper search
in real-world applications. Code for this work is in
https://github.com/Li-Z-Q/PaperRegister.
68a28285a4caabb4320e63ae
Zhuoqun Li
claimed_verified
2025-08-18T06:54:00.985Z
63664c8fa2abcdf2fd6425ed
https://cdn-avatars.huggingface.co/v1/production/uploads/63664c8fa2abcdf2fd6425ed/IywpB0DXZ_twkmZmVSCCD.jpeg
Li Zhuoqun
user
lzq2021
68a28285a4caabb4320e63af
Xuanang Chen
68a28285a4caabb4320e63b0
Hongyu Lin
68a28285a4caabb4320e63b1
Yaojie Lu
68a28285a4caabb4320e63b2
Xianpei Han
68a28285a4caabb4320e63b3
Le Sun
PaperRegister: Boosting Flexible-grained Paper Search via Hierarchical
  Register Indexing
Paper search is an important activity for researchers, typically involving
using a query with description of a topic to find relevant papers. As research
deepens, 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 these systems mainly
collect paper abstracts to construct index of corpus, which lack detailed
information to support retrieval by finer-grained queries. In this work, we
propose PaperRegister, consisted of offline hierarchical indexing and online
adaptive retrieval, transforming traditional abstract-based index into
hierarchical index tree for paper search, thereby supporting queries at
flexible granularity. Experiments on paper search tasks across a range of
granularity demonstrate that PaperRegister achieves the state-of-the-art
performance, and particularly excels in fine-grained scenarios, highlighting
the good potential as an effective solution for flexible-grained paper search
in real-world applications. Code for this work is in
https://github.com/Li-Z-Q/PaperRegister.
PaperRegister enhances paper search by using hierarchical indexing and adaptive retrieval, supporting flexible and fine-grained queries beyond traditional abstract-based systems.
hierarchical indexing adaptive retrieval fine-grained queries