Analysing the Resourcefulness of the Paragraph for Precedence Retrieval
This work addresses the problem of retrieving relevant legal precedents for practitioners, but it is incremental as it builds on existing methods by focusing on paragraph-level analysis.
The paper analyzed paragraph-level information for improving precedence retrieval in legal judgments, finding that paragraph-level methods capture similarity with few interactions and show comparable performance to state-of-the-art methods on benchmark datasets.
Developing methods for extracting relevant legal information to aid legal practitioners is an active research area. In this regard, research efforts are being made by leveraging different kinds of information, such as meta-data, citations, keywords, sentences, paragraphs, etc. Similar to any text document, legal documents are composed of paragraphs. In this paper, we have analyzed the resourcefulness of paragraph-level information in capturing similarity among judgments for improving the performance of precedence retrieval. We found that the paragraph-level methods could capture the similarity among the judgments with only a few paragraph interactions and exhibit more discriminating power over the baseline document-level method. Moreover, the comparison results on two benchmark datasets for the precedence retrieval on the Indian supreme court judgments task show that the paragraph-level methods exhibit comparable performance with the state-of-the-art methods