Incorporating Domain Knowledge for Extractive Summarization of Legal Case Documents
This addresses the challenge of generating high-quality summaries for legal professionals by incorporating expert guidelines, though it is incremental as it builds on existing summarization methods with domain-specific enhancements.
The authors tackled the problem of automatic summarization for legal case documents by proposing DELSumm, an unsupervised algorithm that systematically incorporates domain knowledge from legal experts, and it outperformed strong baselines, including supervised models, in ROUGE scores on Indian Supreme Court documents.
Automatic summarization of legal case documents is an important and practical challenge. Apart from many domain-independent text summarization algorithms that can be used for this purpose, several algorithms have been developed specifically for summarizing legal case documents. However, most of the existing algorithms do not systematically incorporate domain knowledge that specifies what information should ideally be present in a legal case document summary. To address this gap, we propose an unsupervised summarization algorithm DELSumm which is designed to systematically incorporate guidelines from legal experts into an optimization setup. We conduct detailed experiments over case documents from the Indian Supreme Court. The experiments show that our proposed unsupervised method outperforms several strong baselines in terms of ROUGE scores, including both general summarization algorithms and legal-specific ones. In fact, though our proposed algorithm is unsupervised, it outperforms several supervised summarization models that are trained over thousands of document-summary pairs.