DETQUS: Decomposition-Enhanced Transformers for QUery-focused Summarization

• URL: http://arxiv.org/abs/2503.05935v1
• Date: Fri, 07 Mar 2025 21:11:35 GMT
• Title: DETQUS: Decomposition-Enhanced Transformers for QUery-focused Summarization
• Authors: Yasir Khan, Xinlei Wu, Sangpil Youm, Justin Ho, Aryaan Shaikh, Jairo Garciga, Rohan Sharma, Bonnie J. Dorr,
• Abstract summary: We introduce DETQUS (Decomposition-Enhanced Transformers for QUery-focused Summarization), a system designed to improve summarization accuracy.<n>We employ a large language model to reduce table size, retaining only query-relevant columns while preserving essential information.<n>Our approach, equipped with table-based QA model Omnitab, achieves a ROUGE-L score of 0.4437, outperforming the previous state-of-the-art REFACTOR model (ROUGE-L: 0.422)
• Score: 0.6825805890534123
• License: http://creativecommons.org/licenses/by/4.0/
• 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-of-the-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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