In the big data era, text summarizing is essential for reducing long documents into short, easily understood summaries and promoting effective information consumption. An overview of current trends, varieties, and difficulties in text summarizing is provi

Abstract

In the big data era, text summarizing is essential for reducing long documents into short, easily understood summaries and promoting effective information consumption. An overview of current trends, varieties, and difficulties in text summarizing is provided in this study. A thorough literature review is followed by an exploration of the field's many methodologies and approaches, which include models that incorporate both local and global context, hybrid techniques that combine unsupervised and fuzzy logic, and supervised methods that use neural networks. Along with domain-specific, query-based, and generic summary strategies, the study looks at how extraction-based and abstractive summarization functions. It also explores the workings of text summarization algorithms, including Text Rank and Sequence-to-Sequence Modeling, highlighting how well they can summarize textual content. The significance of swarm intelligence in improving text summarization techniques is also covered in the paper. Lastly, it emphasizes the benefits of text summarization, such as time savings, multilingual compatibility, and increased productivity in understanding and information retrieval. This study will help to enhance our knowledge and comprehension of text summarizing techniques and the range of effective uses they may have for managing enormous volumes of textual data.