Developing An NLP Model For Efficient Summarization Of Legal & Financial Documents

Abstract

The proliferation of legal and financial documents in the digital age has necessitated the development of efficient and accurate summarization techniques to manage the overwhelming volume of information. This paper proposes the development of a Natural Language Processing (NLP) model specifically designed for the summarization of legal and financial documents. The proposed model leverages advanced machine learning techniques, including transformer architectures and deep learning algorithms, to generate concise and coherent summaries. These summaries aim to retain the critical information and key insights of the original documents while significantly reducing their length. The model is trained on a large, domain-specific dataset to enhance its understanding of the unique terminology, structures, and nuances inherent in legal and financial texts. Special attention is given to ensuring the model's outputs are not only syntactically accurate but also contextually relevant and legally sound, which is crucial in professional settings. Evaluation metrics such as ROUGE and BLEU scores are employed to assess the performance of the model, alongside human evaluation by legal and financial experts to ensure practical applicability and accuracy.