@inproceedings{zhou-etal-2026-fincards,
title = "{F}in{CARDS}: Card-Based Analyst Reranking for Financial Document Question Answering",
author = "Zhou, Yixi and
Zhang, Fan and
Chen, YU and
Zhang, Haipeng and
Nakov, Preslav and
Xie, Zhuohan",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1244/",
pages = "24836--24852",
ISBN = "979-8-89176-395-1",
abstract = "Financial question answering (QA) over long corporate filings requires evidence to satisfy strict constraints on entities, financial metrics, fiscal periods, and numeric values. However, existing LLM-based rerankers primarily optimize semantic relevance, leading to unstable rankings and opaque decisions on long documents. We propose FINCARDS, a structured reranking framework that reframes financial evidence selection as constraint satisfaction under a finance-aware schema. FINCARDS represents filing chunks and questions using aligned schema fields (entities, metrics, periods, and numeric spans), enabling deterministic field-level matching. Evidence is selected via a multi-stage tournament reranking with stability-aware aggregation, producing auditable decision traces. Across two corporate filing QA benchmarks, FINCARDS substantially improves early-rank retrieval over both lexical and LLM-based reranking baselines, while reducing ranking variance, without requiring model fine-tuning or unpredictable inference budgets. Our code is available at https://github.com/XanderZhou2022/FINCARDS."
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<abstract>Financial question answering (QA) over long corporate filings requires evidence to satisfy strict constraints on entities, financial metrics, fiscal periods, and numeric values. However, existing LLM-based rerankers primarily optimize semantic relevance, leading to unstable rankings and opaque decisions on long documents. We propose FINCARDS, a structured reranking framework that reframes financial evidence selection as constraint satisfaction under a finance-aware schema. FINCARDS represents filing chunks and questions using aligned schema fields (entities, metrics, periods, and numeric spans), enabling deterministic field-level matching. Evidence is selected via a multi-stage tournament reranking with stability-aware aggregation, producing auditable decision traces. Across two corporate filing QA benchmarks, FINCARDS substantially improves early-rank retrieval over both lexical and LLM-based reranking baselines, while reducing ranking variance, without requiring model fine-tuning or unpredictable inference budgets. Our code is available at https://github.com/XanderZhou2022/FINCARDS.</abstract>
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%0 Conference Proceedings
%T FinCARDS: Card-Based Analyst Reranking for Financial Document Question Answering
%A Zhou, Yixi
%A Zhang, Fan
%A Chen, Y. U.
%A Zhang, Haipeng
%A Nakov, Preslav
%A Xie, Zhuohan
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F zhou-etal-2026-fincards
%X Financial question answering (QA) over long corporate filings requires evidence to satisfy strict constraints on entities, financial metrics, fiscal periods, and numeric values. However, existing LLM-based rerankers primarily optimize semantic relevance, leading to unstable rankings and opaque decisions on long documents. We propose FINCARDS, a structured reranking framework that reframes financial evidence selection as constraint satisfaction under a finance-aware schema. FINCARDS represents filing chunks and questions using aligned schema fields (entities, metrics, periods, and numeric spans), enabling deterministic field-level matching. Evidence is selected via a multi-stage tournament reranking with stability-aware aggregation, producing auditable decision traces. Across two corporate filing QA benchmarks, FINCARDS substantially improves early-rank retrieval over both lexical and LLM-based reranking baselines, while reducing ranking variance, without requiring model fine-tuning or unpredictable inference budgets. Our code is available at https://github.com/XanderZhou2022/FINCARDS.
%U https://aclanthology.org/2026.findings-acl.1244/
%P 24836-24852
Markdown (Informal)
[FinCARDS: Card-Based Analyst Reranking for Financial Document Question Answering](https://aclanthology.org/2026.findings-acl.1244/) (Zhou et al., Findings 2026)
ACL