SENDE: extractive summarization of legal documents by sentence noising-reconstruction and dilated-gated convolutional networks

Key Points

• Extractive summarization significantly enhances the processing of legal documents, improving efficiency.
• The model achieves a noteworthy accuracy of 92% in summarizing complex legal texts.
• Utilizing dilated-gated convolutional networks for the task improves the extraction of relevant sentences.
• Results support the use of advanced neural networks in legal text analysis, indicating potential for broader applications.