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@article{192820,
        author = {Swapnali Purushottam Kulthe and Dr. Ganesh Wayal},
        title = {A Comprehensive Review of Deep Learning and Large Language Model Frameworks for Text Summarization, Sentiment Analysis, and Translation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {2192-2206},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192820},
        abstract = {The rapid advancement of deep learning and large language models (LLMs) has fundamentally transformed natural language processing (NLP) over the past decade. From early feature-engineering and traditional machine learning approaches to modern transformer-based and unified language model frameworks, NLP systems have achieved remarkable progress in text summarization, sentiment analysis, multilingual translation, and large-scale opinion mining. However, the fast-paced evolution of methodologies has resulted in a fragmented body of literature, making it challenging to obtain a consolidated and comparative understanding of existing techniques, their strengths, and their limitations. This paper presents a comprehensive review of research published between 2020 and 2025, systematically analyzing more than forty representative studies across key NLP tasks. A structured taxonomy of methodologies is introduced, categorizing approaches into traditional machine learning models, deep neural networks, transformer-based architectures, hybrid deep learning–optimization frameworks, and unified multi-task language model systems. Comparative analysis highlights how performance improvements are accompanied by increased computational complexity, reduced interpretability, and emerging ethical concerns. The review further identifies critical research gaps, including limited multilingual generalization, semantic hallucination in generative models, insufficient modeling of emotional complexity, bias propagation, and the lack of human-centric evaluation metrics. To address these challenges, the paper outlines future research directions beyond 2025, emphasizing fact-aware and explainable NLP, culturally adaptive multilingual models, efficient and sustainable architectures, and unified frameworks for holistic text understanding. By synthesizing methodological trends, comparative insights, and open challenges, this review provides a clear roadmap for developing robust, trustworthy, and human-centered language intelligence systems. The findings aim to support researchers and practitioners in designing next-generation NLP solutions that balance performance, interpretability, efficiency, and ethical responsibility.},
        keywords = {Natural Language Processing, Deep Learning, Large Language Models, Text Summarization, Sentiment Analysis, Multilingual Translation, Hybrid Optimization, Explainable AI},
        month = {February},
        }