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

TL;DR

This paper introduces QueryTableSummarizer++, an advanced end-to-end framework using large language models to generate accurate, query-focused summaries from multi-table data without complex preprocessing, outperforming existing methods.

Contribution

The paper presents a novel generative approach with table-aware pre-training and reinforcement learning, improving multi-table query-focused summarization's accuracy and generalization.

Findings

Significantly outperforms state-of-the-art baselines in BLEU, ROUGE, and F1-score.

Demonstrates scalability and cross-domain generalization.

Shows robustness in handling complex multi-table queries.

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 large language models (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.