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LLM-Powered Framework to Explore Summarized Aggregated Multimedia Vertical Web Search Results

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  • Muhammad Wajeeh Uz Zaman,Umer Rashid,Abdur Rehman Khan

    (Department of Computer Sciences, Quaid-i-Azam University Islamabad, Pakistan.School of Computer Science, Centre for Data Science, Queensland University of Technology, Brisbane, Australia)

Abstract

The exponential growth of multimedia content has shifted users’ information-seeking behavior from lookup-based to exploratory search. To aid exploration, search engines adopted two prominent approaches: presenting results in verticals (web images, videos, news) and integrating Generative AI (GenAI) to enable rapid comprehension. However, integrations like GenAI primarily focus on lookup search by providing basic text summaries of top results, which also hinder users’ ability to explore information through multimedia. Consequently, users make additional navigation efforts (clicking, scrolling, switching verticals), hindering information exploration. In this approach, we propose a framework that summarizes vertical search results into comprehensive documents. The framework is powered by a large language model (LLM) that extracts topics from search results and groups semantically similar multimedia results across verticals into unified topic-based summaries. This unified interaction reduces users' navigation effort and increases interest in exploration. We evaluated our approach using ROUGE across three domains (Movies, Music, and Sports) and conducted a system usability study with 31 participants, using the Bing search engine as a baseline. The proposed system achieved an average ROUGE F1 at: R-1 = 0.67 ± 0.15, R-2 = 0.26 ± 0.17, R-L = 0.60 ± 0.20. The navigation efforts were significantly reduced in terms of clicks (21.5 vs. 30.8, p

Suggested Citation

  • Muhammad Wajeeh Uz Zaman,Umer Rashid,Abdur Rehman Khan, 2026. "LLM-Powered Framework to Explore Summarized Aggregated Multimedia Vertical Web Search Results," International Journal of Innovations in Science & Technology, 50sea, vol. 8(3), pages 01-11, April.
    • Handle: RePEc:abq:ijist1:v:8:y:2026:i:3:p:01-11
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    References listed on IDEAS

    as
    1. Holli-Anne Passmore & Ashley N. Krause, 2023. "The Beyond-Human Natural World: Providing Meaning and Making Meaning," IJERPH, MDPI, vol. 20(12), pages 1-14, June.
    2. Mukherjee, Pubali & Jain, Varsha, 2026. "How lack of choice to opt-out of generative artificial intelligence in traditional search engines drives consumer switching intentions: The mechanism of empowerment," Journal of Retailing and Consumer Services, Elsevier, vol. 88(C).
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