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Human Computer Interaction (HCI): Content Summarization Tools

  • Unique Paper ID: 174424
  • Volume: 11
  • Issue: 10
  • PageNo: 3718-3725
  • Abstract:
  • This project explores various Natural Language Processing (NLP) techniques for document summarization, focusing on both extractive and abstractive methods. Extractive summarization selects key sentences directly from the original text, while abstractive summarization generates new sentences to convey the essential meaning of the content. The study emphasizes user feedback on summary quality, relevance, and customization options to better understand how these methods perform in real-world contexts. Surveys and interviews are conducted to gather insights on user satisfaction, particularly regarding the clarity, accuracy, and personalization of the summaries. By incorporating customization features, the system aims to tailor summaries to individual preferences, making the content more accessible and relevant. The project’s findings will help identify which summarization method serves as the most effective Human-Computer Interaction (HCI) tool, optimizing user engagement by providing concise, relevant, and user-specific summaries.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 10
  • PageNo: 3718-3725

Human Computer Interaction (HCI): Content Summarization Tools

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Impact Factor
8.01 (Year 2024)
UGC Approved
Journal no 47859

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