Majority of India’s population is multilingual; there is a growing need to develop efficient techniques for summarizing text content and voice in various Indian languages. Natural Language Processing (NLP) has come up very useful tool in recent years. NLP is used for processing and understanding textual data and audio 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.

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Multilingual Text and Audio Summarization

  • Anjusha Pimpalshende,
  • Vadlana Baby,
  • Chalumuru Suresh,
  • Challa Sai Venkata Teja

摘要

Majority of India’s population is multilingual; there is a growing need to develop efficient techniques for summarizing text content and voice in various Indian languages. Natural Language Processing (NLP) has come up very useful tool in recent years. NLP is used for processing and understanding textual data and audio 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.