Automatic Text Summarization of Legal Cases: A Hybrid Approach
This addresses the time-consuming task for lawyers in preparing legal briefs, but it is incremental as it builds on existing NLP techniques.
The paper tackles the problem of manual summarization of legal cases by proposing a hybrid method using k-means clustering and tf-idf, achieving results compared to lawyer-prepared summaries using ROGUE evaluation parameters.
Manual Summarization of large bodies of text involves a lot of human effort and time, especially in the legal domain. Lawyers spend a lot of time preparing legal briefs of their clients' case files. Automatic Text summarization is a constantly evolving field of Natural Language Processing(NLP), which is a subdiscipline of the Artificial Intelligence Field. In this paper a hybrid method for automatic text summarization of legal cases using k-means clustering technique and tf-idf(term frequency-inverse document frequency) word vectorizer is proposed. The summary generated by the proposed method is compared using ROGUE evaluation parameters with the case summary as prepared by the lawyer for appeal in court. Further, suggestions for improving the proposed method are also presented.