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    <abstract>Literature review tables are essential for summarizing and comparing collections of scientific papers. In this paper, we study automatic generation of such tables from a pool of papers to satisfy a user’s information need. Building on recent work (Newman et al., 2024), we move beyond oracle settings by (i) simulating well-specified yet schema-agnostic user demands that avoid leaking gold column names or values, (ii) explicitly modeling retrieval noise via semantically related but out-of-scope distractor papers verified by human annotators, and (iii) introducing a lightweight, annotation-free, utilization-oriented evaluation that decomposes utility (schema coverage, unary cell fidelity, pairwise relational consistency) and measures paper selection via a two-way QA procedure (gold→system and system→gold) with recall, precision, and F1. To support reproducible evaluation, we introduce arXiv2Table, a benchmark of 1,957 tables referencing 7,158 papers, with human-verified distractors and rewritten, schema-agnostic user demands. We also develop an iterative, batch-based generation method that co-refines paper filtering and schema over multiple rounds. We validate the evaluation protocol with human audits and cross-evaluator checks. Extensive experiments show that our method consistently improves over strong baselines, while absolute scores remain modest, underscoring the task’s difficulty. Our data and code is available at https://github.com/JHU-CLSP/arXiv2Table.</abstract>
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%0 Conference Proceedings
%T arXiv2Table: Toward Realistic Benchmarking and Evaluation for LLM-Based Literature-Review Table Generation
%A Wang, Weiqi
%A Ou, Jiefu
%A Song, Yangqiu
%A Van Durme, Benjamin
%A Khashabi, Daniel
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F wang-etal-2026-arxiv2table
%X Literature review tables are essential for summarizing and comparing collections of scientific papers. In this paper, we study automatic generation of such tables from a pool of papers to satisfy a user’s information need. Building on recent work (Newman et al., 2024), we move beyond oracle settings by (i) simulating well-specified yet schema-agnostic user demands that avoid leaking gold column names or values, (ii) explicitly modeling retrieval noise via semantically related but out-of-scope distractor papers verified by human annotators, and (iii) introducing a lightweight, annotation-free, utilization-oriented evaluation that decomposes utility (schema coverage, unary cell fidelity, pairwise relational consistency) and measures paper selection via a two-way QA procedure (gold→system and system→gold) with recall, precision, and F1. To support reproducible evaluation, we introduce arXiv2Table, a benchmark of 1,957 tables referencing 7,158 papers, with human-verified distractors and rewritten, schema-agnostic user demands. We also develop an iterative, batch-based generation method that co-refines paper filtering and schema over multiple rounds. We validate the evaluation protocol with human audits and cross-evaluator checks. Extensive experiments show that our method consistently improves over strong baselines, while absolute scores remain modest, underscoring the task’s difficulty. Our data and code is available at https://github.com/JHU-CLSP/arXiv2Table.
%U https://aclanthology.org/2026.acl-long.346/
%P 7602-7624

Markdown (Informal)

[arXiv2Table: Toward Realistic Benchmarking and Evaluation for LLM-Based Literature-Review Table Generation](https://aclanthology.org/2026.acl-long.346/) (Wang et al., ACL 2026)

• arXiv2Table: Toward Realistic Benchmarking and Evaluation for LLM-Based Literature-Review Table Generation (Wang et al., ACL 2026)

ACL

• Weiqi Wang, Jiefu Ou, Yangqiu Song, Benjamin Van Durme, and Daniel Khashabi. 2026. arXiv2Table: Toward Realistic Benchmarking and Evaluation for LLM-Based Literature-Review Table Generation. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7602–7624, San Diego, California, United States. Association for Computational Linguistics.