A Survey on Text and Voice Summarization of Dravidian Languages

As the majority of India's population is multilingual, there is a growing need to develop efficient techniques for summarizing content in various Indian languages. With a significant portion of the population speaking various Indian languages, there will be an increasing demand for efficient techniques to summarize content and voice in these diverse languages. In recent years, Natural Language Processing (NLP) has emerged as a powerful tool for processing and understanding textual data. The digital revolution has revolutionized education, offering an abundance of educational content in Indian languages. However, this vast amount of information can overwhelm learners, making it challenging for them to grasp and retain essential concepts. To address this challenge, our focus is on leveraging the power of NLP to develop a specialized text and voice summarization system for Indian languages, aiming to optimize the learning experience. The challenges posed by Indian languages are they have diverse scripts, different grammar rules, and regional variations. This linguistic understanding will lay the groundwork for designing NLP algorithms that adapt effectively to different languages, ensuring the accuracy and relevance of the summarized content.