@inproceedings{yeniterzi-yeniterzi-2026-genaius,
    title = "{G}en{AI}us at {RAG}4{R}eports 2026: Citation-Aware Compression for Multilingual Report Generation",
    author = "Yeniterzi, Reyyan  and
      Yeniterzi, Suveyda",
    editor = "Yang, Eugene  and
      Lawrie, Dawn  and
      MacAvaney, Sean  and
      Mayfield, James  and
      Soldaini, Luca  and
      Yates, Andrew",
    booktitle = "Proceedings of the 1st Workshop on Multilingual Report Generation via Retrieval Augmented Generation ({RAG}4{R}eports 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.rag4reports-1.11/",
    pages = "83--88",
    ISBN = "979-8-89176-417-0",
    abstract = "This paper describes the GenAIus submission to RAG4Reports 2026 Multilingual Report Generation Task. Our system builds on our earlier TREC RAGTIME pipeline, reusing the evidence preparation stages for overlapping topics, including question generation, multilingual retrieval, nugget generation, and nugget clustering. For RAG4Reports, we focused on the final generation stage and tested a citation-aware compression strategy: generating the long report first from clustered evidence nuggets and then deriving the short report from it, rather than generating both length conditions independently. Our baseline run, which followed the original TREC-style setup, ranked third overall. Our best run, genaius-cluster-gpt4, ranked second overall with an F1 score of 0.5456, achieving the best balance among our submissions between nugget coverage and sentence support. The results suggest that citation-aware compression is a promising strategy for length-constrained, citation-grounded report generation."
}