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PST-Bench: Tracing and Benchmarking the Source of Publications

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arxiv 2402.16009 v1 pith:C255CLBI submitted 2024-02-25 cs.DL cs.CL

classification cs.DLcs.CL
keywords pst-benchsourcedatasetresearcherssciencetracingevolutionvarious
verification ladder T0 review T1 audit T2 compute T3 formal
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Tracing the source of research papers is a fundamental yet challenging task for researchers. The billion-scale citation relations between papers hinder researchers from understanding the evolution of science efficiently. To date, there is still a lack of an accurate and scalable dataset constructed by professional researchers to identify the direct source of their studied papers, based on which automatic algorithms can be developed to expand the evolutionary knowledge of science. In this paper, we study the problem of paper source tracing (PST) and construct a high-quality and ever-increasing dataset PST-Bench in computer science. Based on PST-Bench, we reveal several intriguing discoveries, such as the differing evolution patterns across various topics. An exploration of various methods underscores the hardness of PST-Bench, pinpointing potential directions on this topic. The dataset and codes have been available at https://github.com/THUDM/paper-source-trace.

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Cited by 1 Pith paper

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  1. Adapting Pretrained Language Models for Citation Classification via Self-Supervised Contrastive Learning

    cs.CL 2025-05 conditional novelty 6.0 of 10

    Citss combines sentence-level cropping and keyphrase perturbation with contrastive learning to fine-tune both encoder and decoder language models for citation classification.

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