The task of condensing large chunks of textual information into concise and structured tables has gained attention recently due to the emergence of Large Language Models (LLMs) and their potential benefit for downstream tasks, such as text summarization and text mining. Previous approaches often generate tables that directly replicate information from the text, limiting their applicability in broader contexts, as text-to-table generation in real-life scenarios necessitates information extraction, reasoning, and integration. However, there is a lack of both datasets and methodologies towards this task. In this paper, we introduce LiveSum, a new benchmark dataset created for generating summary tables of competitions based on real-time commentary texts. We evaluate the performances of state-of-the-art LLMs on this task in both fine-tuning and zero-shot settings, and additionally propose a novel pipeline called $T^3$(Text-Tuple-Table) to improve their performances. Extensive experimental results demonstrate that LLMs still struggle with this task even after fine-tuning, while our approach can offer substantial performance gains without explicit training. Further analyses demonstrate that our method exhibits strong generalization abilities, surpassing previous approaches on several other text-to-table datasets. Our code and data can be found at https://github.com/HKUST-KnowComp/LiveSum.

翻译：近年来，随着大语言模型（LLMs）的出现及其在文本摘要和文本挖掘等下游任务中的潜在应用价值，将大量文本信息浓缩为简洁结构化表格的任务逐渐受到关注。现有方法生成的表格往往直接复制文本信息，这限制了其在更广泛场景下的适用性，因为现实场景中的文本到表格生成需要信息提取、推理与整合能力。然而，目前该任务既缺乏专用数据集，也缺少系统方法论。本文提出了LiveSum——一个基于实时解说文本生成赛事摘要表格的全新基准数据集。我们评估了前沿大语言模型在微调和零样本设置下在此任务上的表现，并进一步提出了一种名为$T^3$（文本-元组-表格）的新型流程来提升其性能。大量实验结果表明，即使经过微调，大语言模型在此任务上仍面临困难，而我们的方法无需显式训练即可带来显著的性能提升。进一步分析表明，该方法展现出强大的泛化能力，在多个其他文本到表格数据集上超越了先前方法。我们的代码与数据可在https://github.com/HKUST-KnowComp/LiveSum获取。