Analysis and Performance of Text Summarization Tools Applied on Indian Languages

Text summarization is now essential for gleaning useful information from enormous amounts of data in the age of exponential data expansion. To extract meaningful information from a document’s content, it uses a variety of techniques including neural networks, natural language processing, deep learning, latent semantic analysis, and text rank. Text summary helps in the brief summarization of important information, which is essential in our fast-paced environment. This study examines the many techniques and strategies used in text summarization, concentrating on the two categories of summaries—extractive and abstractive. Additionally, it draws attention to how differently Indian languages like Hindi, Marathi, and Bengali perform. The paper is a useful resource for anybody interested in the most recent developments in text summarizing because it also explains the methods used to extract valuable data or texts. It is anticipated that communication technologies will advance with text summarizing in the future due to the ongoing development of new techniques. Because of these developments, the research and development industry is expanding quickly, making text summarization a crucial tool for obtaining important information from massive amounts of data.