Harnessing Advanced NLP Techniques: Empirical Study on Automated Text Summarization via SpaCy Word Embeddings and Cosine Similarity

As legal professionals and researchers deal with the ever-expanding volume of legal information, the methodology presented in this research paper serves as a valuable tool for enhancing document comprehension and faster information retrieval. The research presented goes deep into the efficacy of an extractive summarization algorithm designed for the nuanced landscape of legal document summarization. Demonstrating the ability to filter key information from a diverse array of legal texts, the algorithm adheres meticulously to predetermined word counts, showcasing its effectiveness in navigating the intricacies of summarization within the legal domain. One unique aspect of this methodology is the use of word embeddings, which are particularly suited for this task and are specifically harnessed to augment the model's capacity in capturing intricate semantic relationships inherent in legal terminology, leading to a higher rogue score value. Results show that as user-requested word limits increase, ROUGE scores increase, highlighting the importance of evaluating summary comprehensiveness based on word limits.