DETQUS : Decomposition-Enhanced Transformers for QU ery-focused Summarization (2025)

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.

## introduction tabular data has become increasingly prevalent in our society, with nearly all businesses employing it to store crucial information. as the amount of collected data increases over time, this has created the need for techniques and systems that allow individuals to analyze and create insights about their data. automatic summarization is an area of research that investigates methods to glean insights from natural language data using various techniques @xcite . the importance of analyzing tabular data combined with the existing summarization research has given rise to a new subset of summarization: queryfocused tabular summarization. query-focused tab-figure 1 : query-focused table summarization with qt-summ, generating a summary from the query: "what is the latest ... intelligence?" ular summarization refers to the task of extracting key points and context from large tables based on a user-provided query @xcite . considering the query-focused table summarization system depicted in figure 1 , when a user inquires about "arrival date" and "basic information," our model evaluates the user's requirements in conjunction with a table lookup and then generates a summary based on these constraints. we consider using neural techniques, particularly transformer-based models, and identify several associated challenges. the first challenge is the inherent token capacity limitation of transformer models. when tables are converted to text and tokenized, they can exceed the input length of the transformer model, causing performance degradation due to truncation from the overly long sequence length. another challenge is processing larger tables and complex queries, which require models to reason across numerous columns, establish meaningful connections, and synthesize coherent summaries from the extracted information. although large language models (llms) exhibit emergent reasoning abilities and greater token capacity, their reasoning abilities still falls short of human performance @xcite . to address these limitations, we introduce de-tqus (decomposition-enhanced transformers for query-focused summarization), a novel system designed to enhance query-based table summarization through tabular decomposition. detqus dynamically restructures tables into smaller, more relevant forms based on the provided query, effectively mitigating token length constraints while preserving critical content. the principle behind this technique is that the model only accesses data relevant to improving performance. this helps mitigate the token limitation of many attention-based methods and the complexity of reasoning over large tables and complex queries. our work builds on the foundational research of @xcite , introducing tabular decomposition based on the given query. we extend this approach to query-focused table summarization, making several key contributions. our main contribution is developing detqus, a system that leverages tabular decomposition to enhance query-based table summarization. detqus outperforms prior baselines while offering a more structured and interpretable approach. we evaluate our method across multiple transformer-based models, demonstrating improvements in summarization quality through a combination of fine-tuned neural architectures and optimized decomposition techniques. section 2 presents related work, followed by a description of our system architecture for tabular summarization in section 3. we present data and experiments in section 4. finally, we discuss insights gleaned from our study and provide conclusions and future directions. ## related work this section outlines the task of query-focused tabular summarization, highlighting how it differs from other table-to-text tasks. we review prior approaches to these tasks and their limitations, comparing them to our method. we also describe the qtsumm dataset which we use for our evaluation. ## results and analyses the results indicate that our approach performs effectively and, in certain cases, surpasses the performance of the prior baseline technique. the analysis, as presented in tables 1 and 2 reveals that llama 3 outperforms other large language models, while omnitab achieves the highest scores in most metrics, including rouge-l and parent. this suggests that the new llama model offers significant advantages over earlier llm architectures for querying tables. in particular, the omnitab model, when used with decomposed tabular data, emerges as the bestperforming model, achieving a rouge-l score of 0.4437 and a parent score of 0.3346. this performance surpasses the previous state-of-theart refactor model, which has a rouge-l score of 0.422, also employing the omnitab model. additionally, it is noteworthy that models such as bart, t5, flan-t5, and omnitab generally exhibit slightly better performance than other llms. this indicates that these models possess strengths particularly suited to the task. ## human evaluation study and error analysis although automatic n-gram overlap metrics like rouge are valuable, they have limitations in evaluating semantically similar text. a qualitative analysis is necessary to better understand any system's strengths and limitations. therefore, we conduct an error analysis and a human evaluation study to gain additional insights into our system. ## conclusion and future work this study presents a novel query-focused approach to tabular summarization, integrating table decomposition with advanced text generation models (t5, flan-t5, bart, and omnitab). we mitigate token limitations of existing models by efficiently handling large and complex tables, thereby improving upon the current state-of-the-art refactor. our best-performing model attains a rouge-l score of 0.4437, setting a new high score for query-focused tabular summarization. by exploring diverse models (t5, flan-t5, bart, omnitab, llama, mixtral, gpt), we gain valuable insights into their capabilities on this task. future research directions include exploring additional models and llms, such as flan-t5 xxl, a larger variant than the one used in this study. improving ensemble techniques by training models on different types of data or queries is another future direction. for example, some models could be trained on simple queries and others on complex queries, or one model could train on the full dataset while others train on the decomposed dataset. this would allow the ensemble model to leverage the respective strengths of individual models. moreover, llama3 demonstrates the highest accuracy in query-based table summarization tasks, as demonstrated by human evaluation, reflecting superior fact extraction capabilities, but omnitab achieves the highest rouge score, indicating stronger overall summarization performance. this result suggests that while llama3 excels in accuracy and detailed fact retrieval, omnitab provides a more coherent and comprehensive summary. to leverage the strengths of both models, future research could explore the development of a hybrid model that combines llama3's precise fact extraction with omnitab's robust table summarization capabilities. such an approach could potentially enhance both the accuracy and overall quality of table summarization, offering a more balanced and effective solution. in scenarios where even a decomposed table exceeds the summarization model's token capacity, additional strategies become necessary. one approach is further decomposition-breaking the table into even smaller, manageable segments. alternatively, hierarchical methods can process large tables by first summarizing individual sections and then combining these summaries into a cohesive final output. techniques such as iterative summarization or chunked processing may also help preserve key information when handling extremely large datasets. evaluating the effectiveness of these approaches is an important direction for future research to enhance model robustness in managing complex, large-scale tabular data. additionally, implementing continuous learning mechanisms may allow models to evolve in response to new data and user feedback without requiring full retraining. this adaptive learning may help maintain the relevance and efficiency of the summarization system @xcite . another promising avenue for future work is to explore evaluation metrics beyond bleu, rouge, bertscore and parent such as bartscore. it utilizes a pre-trained bart model and has demonstrated robust efficiency in evaluating the quality of output summaries by analyzing both lexical overlap and semantic similarity. by applying this, we could assure a more thorough assessment of model performance and produced summaries. by employing these enhancements, we can solidify the utility of our research and push the boundaries of what automated systems can achieve in the realm of intelligent data summarization.