Survey on Techniques for Video Summarization and Audio Generation

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@article{1705201,
      author = {Tanvi Khare, Aditi Mojidra, Prerna Maindarge, Sneha Salunke, Kalyani Waghmare},
      title = {Survey on Techniques for Video Summarization and Audio Generation},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {7},
      number = {5},
      pages = {99-105},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1705201.pdf},
      abstract = {The demand for video summarization is rising, driven by the need for quick and efficient access to essential information within voluminous video content. Simultaneously, there?s a growing necessity for multilingual audio generation to cater to diverse global audiences. Effective summarization tools are essential, enabling users to extract key insights swiftly. Additionally, integrating multilingual audio capabilities ensures that these summarized contents are accessible to speakers of various languages, enhancing inclusivity. In this paper, we present a comprehensive review of the existing literature on machine learning models and evaluation methods that caters to the requirements of the application. Through an extensive survey of scholarly works, we synthesize and evaluate the key findings, methodologies, and theoretical frameworks in the field. Our review not only offers a comprehensive understanding of the current state of research but also identifies gaps and emerging trends, providing valuable insights for future utilization.},
      keywords = {Natural Language Processing (NLP), Video summarization, Multilingual text-to-speech synthesis, Keyframe extraction, Recommendation system, Information extraction},
      month = {November},
  }