Deep-Learning-Based Pre-Training and Refined Tuning for Web Summarization Software

• Published in: IEEE access 2024, Vol.12, p.92120-92129
• Main Authors: Liu, Mingyue, Ma, Zhe, Li, Jiale, Cheng Wu, Ying, Wang, Xukang
• Format: Article
• Language: English
• Subjects: Adaptation models; Datasets; Deep learning; Information retrieval; Natural language processing; Pre-training; Predictive models; Smoothing methods; Task analysis; Training; Tuning; web information extraction; Web services; Webs
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3423662

In the digital age, the rapid growth of web information has made it increasingly challenging for individuals and organizations to effectively explore and extract valuable insights from the vast amount of information available. This paper presents a novel approach to automated web text summarization that combines advanced natural language processing techniques with recent breakthroughs in deep learning. we propose a dual-faceted technique that leverages extensive pre-training on a broad dataset outside the domain, followed by a unique refined tuning process. We introduce a carefully curated dataset that captures the heterogeneous nature of web articles and propose an innovative pre-training and tuning approach that establishes a new state-of-the-art in news summarization. Through extensive experiments and rigorous comparisons against existing models, we demonstrate the superiority of our method, particularly highlighting the crucial role of the refined tuning process in achieving these results. Through rigorous experimentation against state-of-the-art models, we demonstrate the superior performance of our approach, highlighting the significance of their refined tuning process in achieving these results.