Exploring Text Summarization Techniques: A Review of Current Challenges and Future Directions
Abha Kaushik, Shree Harsh Attri, Ravi Shankar Jha · 2024
Text summarization is a significant task in natural language processing (NLP) since it seeks to reduce massive amount of textual information into concise and logical summaries. The rapid expansion of digital content has raised the need for efficient text summarizing strategies. An in-depth evaluation of current trends and future directions in text summarization is provided during the investigations of summarizing techniques. This Survey begins by outlining established methodologies, such as extractive and abstractive methods, and noting their current challenges. The emergence of deep learning models in text summarization such as transformer-based models and encoderdecoder architectures will be discussed. These models have shown inspiring results in abstractive summarization and semantic meaning capture. Additionally, Investigation process carries current advances in text summarization, addressing the issues associated with summarizing information from numerous sources and languages. Along with advancement in text summarization, intriguing future avenue in text summarizing research, such as personalized summarization, domain-specific summarization, and the incorporation of external knowledge sources has also been identified. Consequences of these directions for real-world ap- plications and user preferences are analyzed. Objective is to emphasize the merits and demerits of existing approaches, identify significant difficulties, and propose potential future research directions. Finally, the goal of this review is to inspire and lead the future development of more robust and effective text summarizing algorithms.