RUC Team at S em E val-2025 Task 5: Fast Automated Subject Indexing: A Method Based on Similar Records Matching and Related Subject Ranking (2025)

Abstract: This paper presents MaRSI, an automatic subject indexing method designed to address the limitations of traditional manual indexing and emerging GenAI technologies. Focusing on improving indexing accuracy in cross-lingual contexts and balancing efficiency and accuracy in large-scale datasets, MaRSI mimics human reference learning behavior by constructing semantic indexes from pre-indexed document. It calculates similarity to retrieve relevant references, merges, and reorders their topics to generate index results. Experiments demonstrate that MaRSI outperforms supervised fine-tuning of LLMs on the same dataset, offering advantages in speed, effectiveness, and interpretability.

## introduction with the increasing diversification of academic disciplines and the continuous influx of research outputs, the volume of documents in libraries has grown rapidly, raising the demand for efficient and accurate subject indexing techniques @xcite . libraries use controlled vocabularies such as thesaurus and name authorities to assign subject terms for kinds of documents. however, manual indexing has high cost and low processing efficiency. with the use of artificial intelligence(ai), the automated subject indexing technology improves indexing efficiency. by virtue of its strong semantic processing and generalization capacity, generative al, represented by large language models(llms), provides an automated path for multi-lingual subject indexing in large-scale data. in this context, the tib leibniz information centre for science and technology launched the llms4subjects shared task at semeval-2025, one of the acl 2025 semantic evaluation challenges @xcite . the task encourages exploring llms-based automated subject indexing through fine-tuning, retrieval-augmented generation (rag), chain-of-thought prompting, and few-shot learning. this initiative serves as the motivation for our study. through comparative experiments with multiple ai indexing approaches english and german documents, we propose a deep learning-based embedded indexing solution that enhances automatic indexing by matching similar records and ranking related subjects. the study focuses on two key issues: 1) how to improve indexing accuracy in cross-lingual environments? 2) how to maximize accuracy while ensuring data processing efficiency in large-scale datasets? in general, each automatic subject indexing method has its own strengths and limitations, with no universally perfect solution. therefore, the study proposes a comparative experiment across three representative approaches-semantic embedding, supervised fine-tuning (sft), and retrievalaugmented generation (rag) to explore automatic such solutions from both qualitative and quantitative perspectives 3 methods and design ## conclusion and future research this study compares three automated subject indexing methods: rag, sft, and marsi. it finds that marsi, which emulates the cognitive patterns of human indexers , is an efficient and high-quality method. its performance improves with the richness and diversity of subject terms in the training set. however, indexing results exhibit clear disciplinary variations due to different semantic characteristics across fields. these methods are not mutually exclusive, and future work will explore the synergistic use of semantic embedding, llms, and rule-based reasoning. by adjusting fusion output weights according to disciplinary semantic traits, we aim to enhance the accuracy, efficiency, and consistency of automated subject indexing in libraries.