Citation Worthiness Identification for Fine-Grained Citation Recommendation Systems

Roostaee, Meysam

doi:10.1007/s40998-021-00472-3

Cited by 2 publications

(1 citation statement)

References 52 publications

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“…Zeng et al [7] utilized BiLSTMs and highlighted the importance of adjacent sentence context. In a recent study [8], emphasis was placed on sentence-level citation worthiness, incorporating syntax-based learning and down-sampling analyses.…”

Section: Related Workmentioning

confidence: 99%

CiteCaseLAW: Citation Worthiness Detection in Caselaw for Legal Assistive Writing

Khatri,

Sheik,

Wadhwa

et al. 2023

Frontiers in Artificial Intelligence and Applications

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Complex legal language, filled with jargon, nuanced language semantics, and a high level of domain specificity, poses a significant challenge for automation in handling various legal tasks. In the realm of legal document composition, a pivotal component revolves around accurately referencing case laws and other sources to substantiate assertions and arguments. Understanding the legal domain and identifying appropriate citation context or cite-worthy sentences automatically is challenging. Our research is centered on the issue of citation-worthiness identification of a given sentence. This serves as the initial phase in contemporary citation recommendation systems, aimed at alleviating the effort involved in extracting a suitable array of citation contexts. To address this, we first introduce a labeled dataset comprising 178 million sentences, specifically tailored for detecting citation-worthy content within the legal domain. This dataset is curated from the Caselaw Access Project (CAP) (https://case.law/). We proceeded to assess the performance of a range of deep learning models on this novel dataset. Among the models examined, the domain-specific pre-trained model consistently demonstrated superior performance, achieving an 88% F1-score in the task of detecting citation-worthy material. To enhance our insights, we employed inputXGradient explainable AI techniques to dissect the predictions, thereby identifying the tokens that contribute to specific citation classes.

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Section: Related Workmentioning

confidence: 99%