Improving Scientific Document Retrieval with Academic Concept Index
Jeyun Lee, Junhyoung Lee, Wonbin Kweon, Bowen Jin, Yu Zhang, Susik Yoon, Dongha Lee, Hwanjo Yu, Jiawei Han, Seongku Kang

TL;DR
This paper introduces an academic concept index to improve scientific document retrieval by enhancing query generation and context augmentation, leading to better alignment with scientific concepts and improved retrieval accuracy.
Contribution
It proposes a structured academic concept index and two novel methods, CCQGen and CCExpand, to enhance retrieval by leveraging concept coverage and focus.
Findings
Improved retrieval performance with concept-aware methods
Enhanced query diversity and coverage
Better alignment with scientific concepts
Abstract
Adapting general-domain retrievers to scientific domains is challenging due to the scarcity of large-scale domain-specific relevance annotations and the substantial mismatch in vocabulary and information needs. Recent approaches address these issues through two independent directions that leverage large language models (LLMs): (1) generating synthetic queries for fine-tuning, and (2) generating auxiliary contexts to support relevance matching. However, both directions overlook the diverse academic concepts embedded within scientific documents, often producing redundant or conceptually narrow queries and contexts. To address this limitation, we introduce an academic concept index, which extracts key concepts from papers and organizes them guided by an academic taxonomy. This structured index serves as a foundation for improving both directions. First, we enhance the synthetic query…
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Taxonomy
TopicsTopic Modeling · Information Retrieval and Search Behavior · Advanced Text Analysis Techniques
