@inproceedings{boytsov-etal-2025-end,
    title = "End-to-End Aspect-Guided Review Summarization at Scale",
    author = "Boytsov, Ilya  and
      DeGenova, Vinny  and
      Balyasin, Mikhail  and
      Walt, Joseph  and
      Eusden, Caitlin  and
      Rochat, Marie-Claire  and
      Pierson, Margaret",
    editor = "Potdar, Saloni  and
      Rojas-Barahona, Lina  and
      Montella, Sebastien",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2025",
    address = "Suzhou (China)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-industry.31/",
    pages = "463--471",
    ISBN = "979-8-89176-333-3",
    abstract = "We present a scalable large language model (LLM)-based system that combines aspect-based sentiment analysis (ABSA) with guided summarization to generate concise and interpretable product review summaries. Our approach first extracts and consolidates aspect{--}sentiment pairs from individual reviews, selects the most frequent aspects for each product, and samples representative reviews accordingly. These are used to construct structured prompts that guide the LLM to produce summaries grounded in actual customer feedback. We demonstrate the real-world effectiveness of our system through a large-scale online A/B test. Furthermore, we describe our real-time deployment strategy and release a dataset of 11,8 million anonymized customer reviews covering 92,000 products, including extracted aspects and generated summaries, to support future research in aspect-guided review summarization."
}