Analyzing the Impact of Extractive Summarization Techniques on Legal Text

Legal document summarization refers to the process of consolidating lengthy legal document into a more concise form retaining all the critical aspects. This study aimed to evaluate the effectiveness of extractive text summarization for summarizing legal materials. Various models such as SVM, NB, KB, Winnow, and C4.5 were used to summarize the text, and the ROUGE score was used to evaluate performance. The methodology involved utilizing various strategies and models for summarization, including extractive text summarization, which recognizes relevant chunks of content and rewrites it word by word, resulting in a selection of phrases from the source text. The inclusion of all legal aspects into legal document summarization results in a well-structured form. It was found that extractive summarization is commonly used in legal documents because it recognizes relevant content and produces well-structured summaries that include all legal aspects. The study also suggested that additional strategies can be used to generate summaries through extractive text summarization. The results indicated that C4.5 is the most effective model for decision tree classification. Therefore, it can be concluded that extractive text summarization is an effective method for summarizing legal materials, and C4.5 is a useful model for this purpose. Extractive summary recognizes and reproduces large fragments of a message, while abstractive summary uses language processing to create a more human-like summary. Extractive methods are commonly used in legal documents because abstractive summarization may result in the loss of original content and lacks sufficient data for deep learning. Legal document summary should cover all legal aspects, including judgment record and logical fragments, for better document structure. To evaluate summary text performance, the ROUGE score, precision, recall, and F-Measure were used by counting n-grams, overlapping word pairs, and word sequences, and focusing on text summarization. The study also conducted a survey on the use of extractive text summarization in legal documents. Various techniques were examined, and different modules were used in the process. Extractive summarization was chosen for use in legal documents as it preserves the meaning of the document and utilizes a subset of the text for summarization.