• Dr.K.Suresh
• N Pavan Sashank
• P B Surya Viswanadh
• M A M Basha

In this digital era the procurement of data is growing rapidly and this growth leads to excessive amount of data. This excessive amount of data demands more storage. To reduce the growing storage requirements, summarization of the data will be helpful. Summarization is the process of reducing the original information into minimized form without compromising the key information and the context. Summarization can be carried out in two methods namely Extractive Summarization and Abstractive Summarization. Extractive Summarization approach generates the summary from the source data by computing individual score to each of the sentences. This score is a decision-making parameter that decides whether to include or exclude the sentence from the summarized version or condensed version. Whereas the Abstractive Summarization approach generates the summary by advanced natural language methodologies. There would be no similarity between the input information and summarized information, thus it is not just rearranging and formatting the text as performed in extractive approach. The main aim of the project is to find a subset of data that contains all the important information of the input data set.

Abstractive, RNN, Summarization, Encoder, Decoder, SGD, SoftMax, PUNKT, Stopwords.

"Automatic Abstract Text Summarizer", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 6, page no.b198-b205, June-2021, Available :http://www.jetir.org/papers/JETIR2106170.pdf

"Automatic Abstract Text Summarizer", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 6, page no. ppb198-b205, June-2021, Available at : http://www.jetir.org/papers/JETIR2106170.pdf