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In today’s data-driven world, professionals face the challenge of processing and analyzing vast amount of information presented in diverse formats such as text documents, PDFs, presentations, and audio files. This research work proposes a comprehensive deep learning-based document processing system designed to handle multiple file types, including.txt,.docx,.pdf,.pptx,.mp3, and.wav. Leveraging advanced transformer models like Bidirectional Encoder Representations from Transformers (BERT) and Bidirectional and Auto-Regressive Transformers (BART), the proposed system generates concise summaries, sentiment analyses, and topic categorizations. These capabilities enable users to quickly derive key insights across various domains, including business, healthcare, academia, and media. By streamlining information analysis, this system addresses the growing demand for accurate document processing in data-rich environments.

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SummaryXpert: A Deep Learning Framework for Multi-Format Document Processing

  • 会议论文

  • 发表日期：

    2026年

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  • Reddy Saisindhutheja,
  • P. Tejashwini,
  • M. Shirisha,
  • K. Rajesh Reddy

摘要

In today’s data-driven world, professionals face the challenge of processing and analyzing vast amount of information presented in diverse formats such as text documents, PDFs, presentations, and audio files. This research work proposes a comprehensive deep learning-based document processing system designed to handle multiple file types, including.txt,.docx,.pdf,.pptx,.mp3, and.wav. Leveraging advanced transformer models like Bidirectional Encoder Representations from Transformers (BERT) and Bidirectional and Auto-Regressive Transformers (BART), the proposed system generates concise summaries, sentiment analyses, and topic categorizations. These capabilities enable users to quickly derive key insights across various domains, including business, healthcare, academia, and media. By streamlining information analysis, this system addresses the growing demand for accurate document processing in data-rich environments.