Improving Scientific Document Retrieval with Academic Concept Index
Korea University Seoul South Korea；University of Illinois Urbana-Champaign Champaign United States；Texas A\&M University College Station United States；Yonsei University Seoul South Korea；Pohang University of Science；Korea University；University of Illinois Urbana-Champaign；Texas A\&M University；Yonsei University
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 generation with concept coverage-based generation (CCQGen), which adaptively conditions LLMs on uncovered concepts to generate complementary queries with broader concept coverage. Second, we strengthen the context augmentation with concept-focused auxiliary contexts (CCExpand), which leverages a set of document snippets that serve as concise responses to the concept-aware CCQGen queries. Extensive experiments show that incorporating the academic concept index into both query generation and context augmentation leads to higher-quality queries, better conceptual alignment, and improved retrieval performance.
DocReLM: Mastering Document Retrieval with Language Model
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Fudan University（复旦大学）；Harbin Institute of Technology（哈尔滨工业大学）；The Chinese University of Hong Kong（香港中文大学）；Shanghai Jiaotong University（上海交通大学）；University of Oxford（牛津大学）
Hammer PDF: An Intelligent PDF Reader for Scientific Papers
通过阅读科技论文是科研人员获取学术进展的最重要途径，而科技论文大多是PDF格式的。然而，现有的PDF阅读器，如Adobe Reader和Foxit PDF Reader，通常仅用于通过渲染PDF文件作为一个整体进行阅读，而没有考虑对论文本身的多粒度内容理解。具体来说，以论文作为基本的独立单元，现有的PDF阅读器无法访问关于论文的扩展信息，例如相应的视频，博客和代码。与此同时，他们无法理解论文的学术内容，如术语，作者和引文。为了解决这些问题，我们介绍了锤子PDF，一个智能的PDF阅读器的科学论文。除了基本的阅读功能外，Hammer PDF还具有以下四个创新功能：（1）信息抽取能力，可以定位和标记术语等实体的跨度;（2）信息扩展能力，可以呈现论文的相关学术内容，如引文、参考文献、代码、视频、博客等;（3）内置Hammer Scholar，一个基于从各大学术数据库中收集的学术信息的学术搜索引擎;（4）内置Q&A bot，可以找到有帮助的会议信息。拟议的Hammer PDF Reader可以帮助研究人员，特别是学习计算机科学的研究人员，提高阅读科学论文的效率和体验。我们已经发布了Hammer PDF，可在https://pdf.hammerscholar.net/face上获得。
Research Team Identification Based on Representation Learning of Academic Heterogeneous Information Network
Beijing University of Posts and Telecommunications（北京邮电大学）；School of Economics and Management, Beijing University of Posts and Telecommunications（北京邮电大学经济管理学院）
Enhancing Academic Paper Recommendations Using Fine-Grained Knowledge Entities and Multifaceted Document Embeddings
Department of Information Management（信息管理系）；Nanjing University of Science & Technology（南京理工大学）；School of Computer Engineering（计算机工程学院）；Jiangsu University of Technology（江苏科技大学）；School of Information Management（信息管理学院）；Central China Normal University（中央财经大学）
PairSem: LLM-Guided Pairwise Semantic Matching for Scientific Document Retrieval
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Korea University（高丽大学）；National Institutes of Health（美国国立卫生研究院）；Pohang University of Science and Technology（浦项科技大学）
CLIRudit: Cross-Lingual Information Retrieval of Scientific Documents
CONICET-Universidad de Buenos Aires. Instituto de Ciencias de la Computación (ICC). Buenos Aires, Argentina（CONICET-阿根廷布宜诺斯艾利斯大学。信息计算研究所（ICC）。布宜诺斯艾利斯，阿根廷）；École de bibliothéconomie et des sciences de l’information. Université de Montréal. Montréal, Canada（信息与图书馆科学学院。蒙特利尔大学。蒙特利尔，加拿大）
National University of Singapore（新加坡国立大学）；Tsinghua University（清华大学）；University of Chinese Academy of Sciences（中国科学院大学）；Funstory.ai Limited（Funstory.ai有限公司）
$\texttt{MixGR}$: Enhancing Retriever Generalization for Scientific Domain through Complementary Granularity
Technical University of Darmstadt（达姆施塔特工业大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Washington（华盛顿大学）；University of Pennsylvania（宾夕法尼亚大学）；Tencent AI Lab（腾讯AI实验室）
Multimodal intelligibility of scholarly hypertext: the documentalist's contribution. A required collaboration for serial documentisation in the scientific editorial process
