A legal document summarization framework is described, but the experiments use four non-legal summarization datasets and generic equations, so the claimed judicial efficiency improvements are not established.
Automated Argument Generation from Legal Facts
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abstract
The count of pending cases has shown an exponential rise across nations (e.g., with more than 10 million pending cases in India alone). The main issue lies in the fact that the number of cases submitted to the law system is far greater than the available number of legal professionals present in a country. Given this worldwide context, the utilization of AI technology has gained paramount importance to enhance the efficiency and speed of legal procedures. In this study we partcularly focus on helping legal professionals in the process of analyzing a legal case. Our specific investigation delves into harnessing the generative capabilities of open-sourced large language models to create arguments derived from the facts present in legal cases. Experimental results show that the generated arguments from the best performing method have on average 63% overlap with the benchmark set gold standard annotations.
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Legal Document Summarization: Enhancing Judicial Efficiency through Automation Detection
A legal document summarization framework is described, but the experiments use four non-legal summarization datasets and generic equations, so the claimed judicial efficiency improvements are not established.