Legal Document

The document proposes using conditional random fields (CRFs) to improve legal document summarization. CRFs are applied to segment legal documents into seven labeled rhetorical components. Feature sets are used to improve CRF performance. A term distribution model and structured domain knowledge are then used to extract key sentences for each rhetorical category. The resulting structured summary is found to be 80% accurate compared to ideal summaries generated by experts.