@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://aclanthology.org/2025.sdp-1.27/",
doi = "10.18653/v1/2025.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."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="fathallah-etal-2025-alexunlp">
<titleInfo>
<title>AlexUNLP-FMT at ClimateCheck Shared Task: Hybrid Retrieval with Adaptive Similarity Graph-based Reranking for Climate-related Social Media Claims Fact Checking</title>
</titleInfo>
<name type="personal">
<namePart type="given">Mahmoud</namePart>
<namePart type="family">Fathallah</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nagwa</namePart>
<namePart type="family">El-Makky</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Marwan</namePart>
<namePart type="family">Torki</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Fifth Workshop on Scholarly Document Processing (SDP 2025)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Tirthankar</namePart>
<namePart type="family">Ghosal</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Philipp</namePart>
<namePart type="family">Mayr</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Amanpreet</namePart>
<namePart type="family">Singh</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Aakanksha</namePart>
<namePart type="family">Naik</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Georg</namePart>
<namePart type="family">Rehm</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dayne</namePart>
<namePart type="family">Freitag</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dan</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sonja</namePart>
<namePart type="family">Schimmler</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Anita</namePart>
<namePart type="family">De Waard</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Vienna, Austria</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-265-7</identifier>
</relatedItem>
<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.</abstract>
<identifier type="citekey">fathallah-etal-2025-alexunlp</identifier>
<identifier type="doi">10.18653/v1/2025.sdp-1.27</identifier>
<location>
<url>https://aclanthology.org/2025.sdp-1.27/</url>
</location>
<part>
<date>2025-07</date>
<extent unit="page">
<start>288</start>
<end>292</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T AlexUNLP-FMT at ClimateCheck Shared Task: Hybrid Retrieval with Adaptive Similarity Graph-based Reranking for Climate-related Social Media Claims Fact Checking
%A Fathallah, Mahmoud
%A El-Makky, Nagwa
%A Torki, Marwan
%Y Ghosal, Tirthankar
%Y Mayr, Philipp
%Y Singh, Amanpreet
%Y Naik, Aakanksha
%Y Rehm, Georg
%Y Freitag, Dayne
%Y Li, Dan
%Y Schimmler, Sonja
%Y De Waard, Anita
%S Proceedings of the Fifth Workshop on Scholarly Document Processing (SDP 2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-265-7
%F fathallah-etal-2025-alexunlp
%X 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.
%R 10.18653/v1/2025.sdp-1.27
%U https://aclanthology.org/2025.sdp-1.27/
%U https://doi.org/10.18653/v1/2025.sdp-1.27
%P 288-292
Markdown (Informal)
[AlexUNLP-FMT at ClimateCheck Shared Task: Hybrid Retrieval with Adaptive Similarity Graph-based Reranking for Climate-related Social Media Claims Fact Checking](https://aclanthology.org/2025.sdp-1.27/) (Fathallah et al., sdp 2025)
ACL