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

Computation and Language