BDeedNet: A Deep Learning Framework for Bengali Deed Summarization

Legal documents, also known as deeds, are considered confidential documents for every individual. However, many find it a daunting task to read and navigate through these documents, which often contain a large number of pages. Researchers worldwide are striving to implement models and adopt strategies to address these challenges in the AI era. This study proposes a novel solution to these issues by summarizing Bengali deeds using a deep learning approach. This article employed transformer-based models with Google mT5 and Bangla T5 algorithms to evaluate human-annotated summaries using standard NLP metrics such as BLEU, ROUGE, WIL, WER, and METEOR. This study also introduces "BDeedNet," a specialised dataset created for summarising Bengali legal deed texts, addressing the lack of resources in this area, which is the first of its kind and enabling more precise and meaningful summaries of legal texts on the proposed models. Each model performs well on the BDeedNet dataset with high accuracy and efficacy, with Bangla T5 outperforming other models and yielding the best results, demonstrating significant potential for real-world applications in legal document summarization. This study sets the path for future developments in Bengali legal text processing and adds significant tools for legal and linguistic communities.