NLP-Driven Insight Extractor: Revolutionizing Summarization Techniques

Abstract

Text summarization streamlines the understanding of large volumes of information by generating concise and meaningful summaries. This study explores and compares key summarization approaches, including extractive, abstractive, multimodal, and multilingual techniques. Extractive methods, such as TF-IDF and Text Rank, identify and select important sentences, whereas transformer-based models like BERT and GPT produce fluent, human-like summaries. Multimodal techniques integrate textual and visual data, while multilingual approaches expand summarization capabilities across different languages.
Using tools like VOS viewer and Raw Graphs, this research conducts a bibliometric analysis of emerging trends, evaluates performance with metrics such as ROUGE and BLEU, and examines challenges like computational demands and limitations in low-resource languages. The study provides insights into optimizing summarization strategies, addressing key obstacles, and enhancing efficiency in real-world applications.
The findings highlight the increasing role of NLP in combating information overload and driving innovations in fields like healthcare, journalism, and education. By assessing both the strengths and limitations of existing techniques, this research contributes to the development of more effective summarization models. As AI-powered methods continue to evolve, they hold the potential to revolutionize the way information is processed and consumed across diverse domains.