• Omkar Jagadish Garud
• Vishnu Shendge
• Vishal Fulsundar
• Prof.Poonam Bhawake

The text data online is increasing massively; hence, producing a summarized text document is essential. We can create the summarization of multiple text documents either manually or automatically. A manual approach may be tedious and a time-consuming process. The resulting composition may not be accurate when processing lengthy articles; hence the second approach, i.e., the automated summary generation process, is essential. Training machine learning models using these processes makes space and time-efficient summary generation possible. There are two widely used methods to generate summaries, namely, Extractive summarization and abstractive summarization. The extractive technique scans the original document to find the relevant sentences and extracts only that information from it. The abstractive summarization technique interprets the original text before generating the summary. This process is more complicated, and transformer architecture-based pretrained models are used for comparing the text & developing the outline. This research analysis uses the BBC news dataset to evaluate and compare the results obtained from the machine learning models. Index Terms—Summarization, Natural Language Processing, Transformers, Deep Learning

Text Summarization, Natural Language Processing, Extractive Summarization,Abstractive Summarization,NLP Algorithms ,Text Summarization Techniques,Text Summarization Applications, Evaluation Metrics for Summarizers (e.g., ROUGE, BLEU) ,Challenges in Text Summarization ,NLP-based Summarization, Models Training NLP Models for Summarization ,Real-time Summarization

"Text Summarization in Natural Language processing(NLP)", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 10, page no.h14-h21, October-2023, Available :http://www.jetir.org/papers/JETIR2310603.pdf

"Text Summarization in Natural Language processing(NLP)", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 10, page no. pph14-h21, October-2023, Available at : http://www.jetir.org/papers/JETIR2310603.pdf