Study of Various Text Summarization Methods

This paper presents an analysis of various text summarization methods used to condense lengthy pieces of text into shorter versions while retaining the most relevant information. The study reviews different approaches to text summarization, including extraction-based and abstraction-based methods. These techniques are assessed in terms of accuracy, fluency, and coherence, using a variety of metrics such as ROUGE, BLEU, and F1 measures. The suggested model may efficiently produce a smaller body of text that is both accurate in both the semantic sense and linguistic sense by comprehending the entire text and framing it on its own. The findings of this study can help researchers and practitioners in the field of NLP to choose the most suitable text summarization method for their particular application.