  Computing similarity between two legal documents is an important and
challenging task in the domain of Legal Information Retrieval. Finding similar
legal documents has many applications in downstream tasks, including prior-case
retrieval, recommendation of legal articles, and so on. Prior works have
proposed two broad ways of measuring similarity between legal documents -
analyzing the precedent citation network, and measuring similarity based on
textual content similarity measures. But there has not been a comprehensive
comparison of these existing methods on a common platform. In this paper, we
perform the first systematic analysis of the existing methods. In addition, we
explore two promising new similarity computation methods - one text-based and
the other based on network embeddings, which have not been considered till now.
