Reasoning-Aware Query-Focused Summarization over Multi-Table Data (2412.08970v1)

Abstract: Query-focused summarization over multi-table data is a challenging yet critical task for extracting precise and relevant information from structured data. Existing methods often rely on complex preprocessing steps and struggle to generalize across domains or handle the logical reasoning required for multi-table queries. In this paper, we propose QueryTableSummarizer++, an end-to-end generative framework leveraging LLMs enhanced with table-aware pre-training, query-aligned fine-tuning, and reinforcement learning with feedback. Our method eliminates the need for intermediate serialization steps and directly generates query-relevant summaries. Experiments on a benchmark dataset demonstrate that QueryTableSummarizer++ significantly outperforms state-of-the-art baselines in terms of BLEU, ROUGE, and F1-score. Additional analyses highlight its scalability, generalization across domains, and robust handling of complex queries. Human evaluation further validates the superior quality and practical applicability of the generated summaries, establishing QueryTableSummarizer++ as a highly effective solution for multi-table summarization tasks.

• The paper introduces QueryTableSummarizer++, an end-to-end generative framework that uses table-aware pre-training, query-aligned fine-tuning, and reinforcement learning to improve query-focused summarization over multi-table data.
• Evaluations on a new benchmark show QueryTableSummarizer++ significantly outperforms state-of-the-art methods, achieving up to a 10% increase in metrics like ROUGE and F1-score.
• This framework offers practical implications for report generation and data-driven decision-making by generalizing across domains and scaling with complex multi-table data.

Reasoning-Aware Query-Focused Summarization over Multi-Table Data

The paper "Reasoning-Aware Query-Focused Summarization over Multi-Table Data" presents a substantial contribution to the field of NLP, particularly in the domain of automated summarization of structured data. It addresses the significant challenge of generating query-specific summaries from complex multi-table datasets. The proposed framework, QueryTableSummarizer++, leverages advancements in LLMs to enhance performance on this task.

The authors identify several limitations of existing methods, such as the dependency on cumbersome preprocessing steps that may lead to information loss and difficulties in generalizing across diverse data formats. Traditional approaches typically miss capturing intricate inter-table relationships, crucial for generating contextually coherent summaries.

Methodology

QueryTableSummarizer++ builds on the capabilities of LLMs through a novel end-to-end generative framework. It incorporates three primary innovations:

1. Table-Aware Pre-Training: This phase enhances the LLM's comprehension of tabular data by introducing tasks focused on understanding row-column relationships and predicting inter-table relationships. This pre-training aims to imbue the model with the ability to deduce implicit connections between different tables—vital for reasoning in multi-table contexts.
2. Query-Aligned Fine-Tuning: The fine-tuning process refines the model to generate summaries aligned with specific queries. It utilizes a contrastive learning approach to strengthen the model's ability to discern relevant table content, thus ensuring the generated summaries are precise and relevant.
3. Reinforcement Learning with Feedback: Incorporation of reinforcement learning aids in optimizing the summaries based on metrics such as relevance, coherence, and succinctness. By providing feedback-driven learning, this module enhances the model's capacity to produce high-quality summaries.

Experimental Evaluation

The authors evaluate QueryTableSummarizer++ on a newly constructed benchmark dataset encompassing various domains like healthcare and finance, with diverse table relationships and query structures. Using evaluation metrics such as BLEU, ROUGE, and F1-score, the framework demonstrates significant performance improvements over existing state-of-the-art techniques, achieving up to a 10% increase in these metrics.

Results and Analysis

The comprehensive experimental setup includes comparisons with several baseline models. QueryTableSummarizer++ consistently outperforms these methods, as evident in Table 1 of the document. An ablation paper highlights the critical contributions of the table-aware pre-training and reinforcement learning stages, indicating a marked drop in performance when these components are omitted.

Human evaluations further attest to the enhanced relevance, coherence, and conciseness of the generated summaries compared to baseline methods, as reflected in Table 2 of the human evaluation metrics.

Implications and Future Speculation

The methodology proposed in this paper represents a significant stride toward more accurate and coherent summarization of multi-table data, which has practical implications across various uses, such as report generation and data-driven decision-making in enterprise environments. The ability of QueryTableSummarizer++ to generalize across domains, deal with complex queries, and scale with an increasing number of tables highlights its potential utility in real-world applications.

Future research can explore refining the model to address observed errors, such as handling redundant content and ambiguous table relationships. Moreover, the theoretical underpinnings of integrating structured data with LLMs can be further examined to enhance their application scope in AI-driven data analytics and summarization.

In conclusion, QueryTableSummarizer++ advances the field of NLP by effectively tackling the challenges associated with query-focused summarization over multi-table data, paving the way for more sophisticated and scalable AI tools capable of processing and synthesizing information from structured datasets.