• Sahil Tomar
• Kartik Hooda
• Vineet Mehan

This research article investigates the pivotal role of Natural Language Processing (NLP) in achieving semantic harmony for context-aware document summarization. With a focus on semantic analysis, coherence enhancement, and multi-domain adaptability, the study aims to develop robust NLP algorithms that preserve the original meaning of documents while ensuring coherence and adaptability across diverse domains. The analysis begins with exploring semantic analysis techniques, including word embeddings and semantic similarity measures, to capture contextual nuances effectively. Coherence enhancement strategies, such as sentence reordering and coreference resolution, are implemented to maintain a cohesive narrative flow in generated summaries. Additionally, the research addresses the challenge of multi-domain adaptability by employing transfer learning and domain-specific feature engineering to enable NLP systems to seamlessly adapt to various domains, including news, research, and legal texts.

Natural Language Processing (NLP), Semantic Analysis, Document Summarization, Coherence Enhancement, Multi-Domain Adaptability, Context-Aware, Semantic Harmony

" Semantic Harmony: NLP for Context-Aware Document Summarization", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.a237-a241, May-2024, Available :http://www.jetir.org/papers/JETIR2405030.pdf

" Semantic Harmony: NLP for Context-Aware Document Summarization", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppa237-a241, May-2024, Available at : http://www.jetir.org/papers/JETIR2405030.pdf