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The task of Legal Statute Identification (LSI) aims to identify the legalstatutes that are relevant to a given description of Facts or evidence of alegal case. Existing methods only utilize the textual content of Facts andlegal articles to guide such a task. However, the citation network among casedocuments and legal statutes is a rich source of additional information, whichis not considered by existing models. In this work, we take the first steptowards utilising both the text and the legal citation network for the LSItask. We curate a large novel dataset for this task, including Facts of casesfrom several major Indian Courts of Law, and statutes from the Indian PenalCode (IPC). Modeling the statutes and training documents as a heterogeneousgraph, our proposed model LeSICiN can learn rich textual and graphicalfeatures, and can also tune itself to correlate these features. Thereafter, themodel can be used to inductively predict links between test documents (newnodes whose graphical features are not available to the model) and statutes(existing nodes). Extensive experiments on the dataset show that our modelcomfortably outperforms several state-of-the-art baselines, by exploiting thegraphical structure along with textual features. The dataset and our codes areavailable at https://github.com/Law-AI/LeSICiN.

LeSICiN: A Heterogeneous Graph-based Approach for Automatic Legal Statute Identification from Indian Legal Documents