@inproceedings{khan-etal-2025-detqus,
title = "{DETQUS}: Decomposition-Enhanced Transformers for {QU}ery-focused Summarization",
author = "Khan, Yasir and
Wu, Xinlei and
Youm, Sangpil and
Ho, Justin and
Shaikh, Aryaan Mehboob and
Garciga, Jairo and
Sharma, Rohan and
Dorr, Bonnie J",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.138/",
doi = "10.18653/v1/2025.naacl-long.138",
pages = "2720--2731",
ISBN = "979-8-89176-189-6",
abstract = "Query-focused tabular summarization is an emerging task in table-to-text generation that synthesizes a summary response from tabular data based on user queries. Traditional transformer-based approaches face challenges due to token limitations and the complexity of reasoning over large tables. To address these challenges, we introduce DETQUS (Decomposition-Enhanced Transformers for QUery-focused Summarization), a system designed to improve summarization accuracy by leveraging tabular decomposition alongside a fine-tuned encoder-decoder model. DETQUS employs a large language model to selectively reduce table size, retaining only query-relevant columns while preserving essential information. This strategy enables more efficient processing of large tables and enhances summary quality. Our approach, equipped with table-based QA model Omnitab, achieves a ROUGE-L score of 0.4437, outperforming the previous state-ofthe- art REFACTOR model (ROUGE-L: 0.422). These results highlight DETQUS as a scalable and effective solution for query-focused tabular summarization, offering a structured alternative to more complex architectures."
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<abstract>Query-focused tabular summarization is an emerging task in table-to-text generation that synthesizes a summary response from tabular data based on user queries. Traditional transformer-based approaches face challenges due to token limitations and the complexity of reasoning over large tables. To address these challenges, we introduce DETQUS (Decomposition-Enhanced Transformers for QUery-focused Summarization), a system designed to improve summarization accuracy by leveraging tabular decomposition alongside a fine-tuned encoder-decoder model. DETQUS employs a large language model to selectively reduce table size, retaining only query-relevant columns while preserving essential information. This strategy enables more efficient processing of large tables and enhances summary quality. Our approach, equipped with table-based QA model Omnitab, achieves a ROUGE-L score of 0.4437, outperforming the previous state-ofthe- art REFACTOR model (ROUGE-L: 0.422). These results highlight DETQUS as a scalable and effective solution for query-focused tabular summarization, offering a structured alternative to more complex architectures.</abstract>
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%0 Conference Proceedings
%T DETQUS: Decomposition-Enhanced Transformers for QUery-focused Summarization
%A Khan, Yasir
%A Wu, Xinlei
%A Youm, Sangpil
%A Ho, Justin
%A Shaikh, Aryaan Mehboob
%A Garciga, Jairo
%A Sharma, Rohan
%A Dorr, Bonnie J.
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F khan-etal-2025-detqus
%X Query-focused tabular summarization is an emerging task in table-to-text generation that synthesizes a summary response from tabular data based on user queries. Traditional transformer-based approaches face challenges due to token limitations and the complexity of reasoning over large tables. To address these challenges, we introduce DETQUS (Decomposition-Enhanced Transformers for QUery-focused Summarization), a system designed to improve summarization accuracy by leveraging tabular decomposition alongside a fine-tuned encoder-decoder model. DETQUS employs a large language model to selectively reduce table size, retaining only query-relevant columns while preserving essential information. This strategy enables more efficient processing of large tables and enhances summary quality. Our approach, equipped with table-based QA model Omnitab, achieves a ROUGE-L score of 0.4437, outperforming the previous state-ofthe- art REFACTOR model (ROUGE-L: 0.422). These results highlight DETQUS as a scalable and effective solution for query-focused tabular summarization, offering a structured alternative to more complex architectures.
%R 10.18653/v1/2025.naacl-long.138
%U https://aclanthology.org/2025.naacl-long.138/
%U https://doi.org/10.18653/v1/2025.naacl-long.138
%P 2720-2731
Markdown (Informal)
[DETQUS: Decomposition-Enhanced Transformers for QUery-focused Summarization](https://aclanthology.org/2025.naacl-long.138/) (Khan et al., NAACL 2025)
ACL
- Yasir Khan, Xinlei Wu, Sangpil Youm, Justin Ho, Aryaan Mehboob Shaikh, Jairo Garciga, Rohan Sharma, and Bonnie J Dorr. 2025. DETQUS: Decomposition-Enhanced Transformers for QUery-focused Summarization. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 2720–2731, Albuquerque, New Mexico. Association for Computational Linguistics.