@inproceedings{liu-etal-2025-ai,
    title = "{AI}-Press: A Multi-Agent News Generating and Feedback Simulation System Powered by Large Language Models",
    author = "Liu, Xiawei  and
      Yang, Shiyue  and
      Zhang, Xinnong  and
      Kuang, Haoyu  and
      Sun, Libo  and
      Yang, Yihang  and
      Chen, Siming  and
      Huang, Xuanjing  and
      Wei, Zhongyu",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven  and
      Mather, Brodie  and
      Dras, Mark",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics: System Demonstrations",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2025.coling-demos.8/",
    pages = "63--82",
    abstract = "We introduce AI-Press, an automated news drafting and polishing system based on multi-agent collaboration and Retrieval-Augmented Generation. We develop a feedback simulation system that generates public responses considering demographic distributions. Demo link: https://youtu.be/TmjfJrbzaRU"
}