Multi-Document Text Summarization Techniques: Trends, Challenges, and Future Directions

This study reviews multi-document text summarization (MTDS) approaches, focusing on the evolution of methodologies and neural network advancements. With the growing demand for effective summarization in the era of big data, MTDS has become critical. A comprehensive review of MTDS approaches is essential for understanding existing techniques, identifying gaps, and guiding future research in this rapidly evolving field. Approaches are categorized into traditional, neural network-based, and transformer-based models. The strengths and limitations of techniques like convolutional (CNN), recurrent (RNN), and transformer architectures are analyzed, highlighting their impact on summarization accuracy and efficiency. Key advancements, such as attention mechanisms and pre-trained language models, are examined for their role in enhancing performance. The study synthesizes findings to identify trends, challenges, and future directions, offering insights for advancing MTDS research.