• Dr Abinash Tripathy
• Sri Sai Sundareswar Pullela
• Mounika Kola
• Sai Raj Kasa

The automatic text summarization system generates a summary, i.e. short-length text that includes all the important information of the document without losing any of the original contexts of the text. With the large amounts of information being generated and stored on servers and made easily accessible through the internet, text summarization is an important and potent tool to interpret all the textual information. The summary can be generated through extractive as well as abstractive methods. Abstractive methods are highly complex as they need extensive natural language processing. Therefore, the research community is focusing more on extractive summaries, trying to achieve more coherent and meaningful summaries. For a decade, several extractive approaches have been developed for an automatic summary generation that implements several machine learning and optimization techniques. Despite all the proposed methods, the generated summaries are still far away from human-generated summaries. Most research focuses on the extractive approach. It is required to focus more on the abstractive and hybrid approaches. In this paper, we address the automatic summarization task. Recent research works on extractive-summary generation employ some heuristics, but few works indicate how to select the relevant features. We will present a summarization procedure based on the application of trainable Machine Learning algorithms which employs a set of features extracted directly from the original text.

Extractive Summarization, Abstractive Summarization, Natural Language Processing, Machine Learning

"Automatic Text Summarization in the perspective of context preservation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 5, page no.j327-j332, May-2022, Available :http://www.jetir.org/papers/JETIR2205A43.pdf

"Automatic Text Summarization in the perspective of context preservation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. ppj327-j332, May-2022, Available at : http://www.jetir.org/papers/JETIR2205A43.pdf