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Though many algorithms can be used to automatically summarize legal casedecisions, most fail to incorporate domain knowledge about how importantsentences in a legal decision relate to a representation of its documentstructure. For example, analysis of a legal case summarization datasetdemonstrates that sentences serving different types of argumentative roles inthe decision appear in different sections of the document. In this work, wepropose an unsupervised graph-based ranking model that uses a reweightingalgorithm to exploit properties of the document structure of legal casedecisions. We also explore the impact of using different methods to compute thedocument structure. Results on the Canadian Legal Case Law dataset show thatour proposed method outperforms several strong baselines.

Computing and Exploiting Document Structure to Improve Unsupervised Extractive Summarization of Legal Case Decisions