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@article{204454,
        author = {Himaja BM and Adithyan V Nair},
        title = {AI Powered Web Content Summarizer},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {114-117},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204454},
        abstract = {The exponential increase in digital information necessitates the development of effective tools capable of deriving meaningful insights from extensive text corpora. This paper presents the AIWOCS (AI Web Content Summarizer and Analyzer), a web-based application designed to generate concise and context-aware summaries from multiple input sources, including raw text, web URLs, and uploaded documents.
The system utilizes transformer-based AI models through external inference APIs to perform abstractive summarization. To handle large inputs efficiently, a chunk-based processing mechanism is implemented, enabling scalable summarization without loss of context. Additionally, the system provides keyword extraction and summary evaluation metrics, such as compression ratio and word count analysis.
The application is developed using a Node.js backend and an interactive frontend interface, enabling real-time summarization. The experimental results demonstrate that the system produces accurate, readable, and structured summaries across different types of input data. The proposed system highlights the practical use of modern AI techniques for content analysis and information compression.},
        keywords = {AI summarization, natural language processing, web content extraction, transformer models, text analysis},
        month = {June},
        }