Corpus for Automatic Structuring of Legal Documents
This work addresses the need for automated processing of legal documents in populous countries with growing case backlogs, though it is incremental as it focuses on corpus creation and baseline models.
The authors tackled the problem of organizing legal documents by introducing a new annotated corpus of English legal judgments segmented into rhetorical roles, and developed baseline models for predicting these roles, showing applications that improved summarization and judgment prediction performance.
In populous countries, pending legal cases have been growing exponentially. There is a need for developing techniques for processing and organizing legal documents. In this paper, we introduce a new corpus for structuring legal documents. In particular, we introduce a corpus of legal judgment documents in English that are segmented into topical and coherent parts. Each of these parts is annotated with a label coming from a list of pre-defined Rhetorical Roles. We develop baseline models for automatically predicting rhetorical roles in a legal document based on the annotated corpus. Further, we show the application of rhetorical roles to improve performance on the tasks of summarization and legal judgment prediction. We release the corpus and baseline model code along with the paper.