通过分解表格提升查询摘要精度，让大表处理更高效。
DETQUS: Decomposition-Enhanced Transformers for QUery-focused Summarization
- 用大模型筛选相关列，动态压缩表格减少冗余。
- 在Omnitab数据集上ROUGE-L达0.4437，优于前代模型。
- 适合需要精准响应复杂表格查询的场景。
查询聚焦型表格摘要是一项新兴的表格到文本生成任务，旨在根据用户查询从表格数据中生成摘要。传统基于Transformer的方法受限于令牌数量及对大型表格进行推理的复杂性。为此，我们提出DETQUS（面向查询聚焦摘要的分解增强型Transformer），通过结合表格分解与微调的编码器-解码器模型，提升摘要准确性。DETQUS利用大语言模型选择性地缩小表格规模，仅保留与查询相关的列，同时保持关键信息。该策略使大规模表格处理更高效，并提升摘要质量。系统配备基于表格的问答模型Omnitab，ROUGE-L得分达到0.4437，优于此前最优模型REFACTOR（ROUGE-L: 0.422）。结果表明，DETQUS是一种可扩展且高效的查询聚焦表格摘要解决方案，为复杂架构提供了结构化替代方案。
原文摘要 · Abstract (English)
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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