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@article{174424,
        author = {Garv Porwal and Anushree Paul and Anish Singh and Devanshu Soni and Himangshu Deep Kalita and Dr. A Kannagi},
        title = {Human Computer Interaction (HCI): Content  Summarization Tools},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {3718-3725},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174424},
        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.},
        keywords = {Human computer interaction, hci, ChatGPT, NLP, machine learning, gemini, copilot, HCI.},
        month = {March},
        }