@inproceedings{adhikary-etal-2025-revieweaver,
    title = "{R}evie{W}eaver: Weaving Together Review Insights by Leveraging {LLM}s and Semantic Similarity",
    author = "Adhikary, Jiban  and
      Alqudah, Mohammad  and
      Udayashankar, Arun Palghat",
    editor = "Chen, Weizhu  and
      Yang, Yi  and
      Kachuee, Mohammad  and
      Fu, Xue-Yong",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Author-Pages-WenzhengZhang-ZhengyanShi-ShuYang/2025.naacl-industry.36/",
    doi = "10.18653/v1/2025.naacl-industry.36",
    pages = "431--448",
    ISBN = "979-8-89176-194-0",
    abstract = "With the rise of online retail, customer reviews have become a critical factor in shaping purchasing decisions. The sheer volume of customer reviews being generated continuously presents a challenge for consumers who must sift through an overwhelming amount of feedback. To address this issue, we introduce RevieWeaver, a novel framework that extracts key product features and provides concise review summaries. Our innovative approach not only scales efficiently to 30 million reviews but also ensures reproducibility and controllability. Moreover, it delivers unbiased and reliable assessments of products that accurately reflect the input reviews."
}