@inproceedings{fathallah-etal-2025-alexunlp,
    title = "{A}lex{UNLP}-{FMT} at {C}limate{C}heck Shared Task: Hybrid Retrieval with Adaptive Similarity Graph-based Reranking for Climate-related Social Media Claims Fact Checking",
    author = "Fathallah, Mahmoud  and
      El-Makky, Nagwa  and
      Torki, Marwan",
    editor = "Ghosal, Tirthankar  and
      Mayr, Philipp  and
      Singh, Amanpreet  and
      Naik, Aakanksha  and
      Rehm, Georg  and
      Freitag, Dayne  and
      Li, Dan  and
      Schimmler, Sonja  and
      De Waard, Anita",
    booktitle = "Proceedings of the Fifth Workshop on Scholarly Document Processing (SDP 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.sdp-1.27/",
    pages = "288--292",
    ISBN = "979-8-89176-265-7",
    abstract = "In this paper, we describe our work done in the ClimateCheck shared task at the Scholarly document processing (SDP) workshop, ACL 2025. We focused on subtask 1: Abstracts Retrieval. The task involved retrieving relevant paper abstracts from a large corpus to verify claims made on social media about climate change. We explored various retrieval and ranking techniques, including fine-tuning transformer-based dense retrievers, sparse retrieval methods, and reranking using cross-encoder models. Our final and best-performing system utilizes a hybrid retrieval approach combining BM25 sparse retrieval and a fine-tuned Stella model for dense retrieval, followed by an MSMARCO trained minilm cross-encoder model for ranking. We adapt an iterative graph-based re-ranking approach leveraging a document similarity graph built for the document corpus to dynamically update candidate pool for reranking. This system achieved a score of 0.415 on the final test set for subtask 1, securing 3rd place in the final leader board."
}