Deep Learning based Text Abstraction

Abstract

Abstract: Text abstraction plays a vital role in extracting crucial information from large textual datasets. With the advancements in deep learning, the application of transformer models has shown great potential in the field of text abstraction. This technical paper presents a comprehensive study on "Deep Learning-Based Text Abstraction" with a specific focus on utilizing transformer models for effective abstraction. The paper begins by providing an overview of text abstraction and its significance in handling extensive amounts of textual data while preserving essential information. It then delves into the fundamentals of transformer models, explaining their architecture and mechanisms, particularly attention mechanisms that enable capturing contextual relationships within text. The main contribution of this paper lies in the implementation of text abstraction using transformer models. It discusses the utilization of pre-trained transformer models and their adaptation for text abstraction tasks. The paper elaborates on techniques like fine-tuning and transfer learning to optimize the transformer models for text abstraction. Furthermore, the paper presents a detailed experimental setup, including the selection of datasets, evaluation metrics, and training procedures. It discusses the performance evaluation of the implemented transformer-based text abstraction system, comparing it against existing techniques and benchmark datasets. The results and analysis section showcases the effectiveness and efficiency of the proposed approach, highlighting improvements achieved in terms of extraction accuracy, summarization quality, and computational efficiency. Finally, the paper concludes by outlining potential areas for future research and development in deep learning-based text abstraction using transformer models. Overall, this technical paper provides a comprehensive study on deep learning-based text abstraction, with a focus on transformer models. It serves as a valuable resource for researchers and practitioners interested in leveraging transformers for efficient and accurate text abstraction, opening avenues for advancements in natural language processing and information extraction.