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arXiv · 2602.05413

SciDef: Datasets and Tools for Automated Definition Extraction from Scientific Literature with LLMs

Filip Kučera·Christoph Mandl·Isao Echizen·Radu Timofte·Timo Spinde
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Abstract

Scientific concepts are often defined inconsistently across papers, making it difficult to compare findings, reuse terminology, and build reliable downstream resources. We present SciDef, a resource suite for scientific definition extraction. The suite contains DefExtra, a benchmark of 268 human-validated author-stated definitions from 75 academic papers; DefSim, 60 human-labeled definition-pair similarity judgments; and an open LLM-based pipeline for PDF preprocessing, chunking, definition extraction, prompt optimization, and evaluation. We validate the resources by benchmarking 16 language models across prompting strategies and chunking schemes. The strongest set-level configuration achieves a score of 0.397, while the highest-coverage configuration matches at least one prediction to 86.4% of gold definitions but over-generates candidate definitions. We further show that an NLI-based matching metric agrees strongly with human DefSim judgments. These results position SciDef as a reusable benchmark and tooling layer for definition-centric literature analysis, while highlighting relevance-aware filtering as the key bottleneck for fully automatic definition extraction. Code & datasets are available at https://github.com/Media-Bias-Group/SciDef.

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Filip Kučera, Christoph Mandl, Isao Echizen, Radu Timofte, Timo Spinde. 2026-02-05. SciDef: Datasets and Tools for Automated Definition Extraction from Scientific Literature with LLMs. https://arxiv.org/abs/2602.05413

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