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[2602.14889] Web-Scale Multimodal Summarization using CLIP-Based Semantic Alignment
Summary
The paper presents a framework for web-scale multimodal summarization that integrates text and image data using CLIP-based semantic alignment, enhancing retrieval and summarization capabilities.
Why It Matters
This research is significant as it addresses the growing need for efficient summarization of diverse web content, leveraging advanced machine learning techniques to improve the accessibility and relevance of information retrieval in a multimodal context.
Key Takeaways
- Introduces a lightweight framework for multimodal summarization combining text and images.
- Utilizes a fine-tuned CLIP model for semantic alignment to enhance retrieval accuracy.
- Supports configurable parameters for user-defined summarization tasks.
- Demonstrates strong performance metrics including ROC-AUC of 0.9270 and accuracy of 96.99%.
- Provides a deployable tool for integrating language, retrieval, and vision models.
Computer Science > Machine Learning arXiv:2602.14889 (cs) [Submitted on 16 Feb 2026] Title:Web-Scale Multimodal Summarization using CLIP-Based Semantic Alignment Authors:Mounvik K, N Harshit View a PDF of the paper titled Web-Scale Multimodal Summarization using CLIP-Based Semantic Alignment, by Mounvik K and 1 other authors View PDF HTML (experimental) Abstract:We introduce Web-Scale Multimodal Summarization, a lightweight framework for generating summaries by combining retrieved text and image data from web sources. Given a user-defined topic, the system performs parallel web, news, and image searches. Retrieved images are ranked using a fine-tuned CLIP model to measure semantic alignment with topic and text. Optional BLIP captioning enables image-only summaries for stronger multimodal this http URL pipeline supports features such as adjustable fetch limits, semantic filtering, summary styling, and downloading structured outputs. We expose the system via a Gradio-based API with controllable parameters and preconfigured this http URL on 500 image-caption pairs with 20:1 contrastive negatives yields a ROC-AUC of 0.9270, an F1-score of 0.6504, and an accuracy of 96.99%, demonstrating strong multimodal alignment. This work provides a configurable, deployable tool for web-scale summarization that integrates language, retrieval, and vision models in a user-extensible pipeline. Subjects: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Emerging Technol...
