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-TTT.

翻译：将大段文本信息压缩为简洁结构化表格的任务，近年来因大型语言模型（LLMs）的兴起及其在文本摘要、文本挖掘等下游任务中的潜在价值而备受关注。现有方法通常直接生成复制文本信息的表格，这限制了其在更广泛场景中的适用性——真实场景中的文本到表格生成需要信息抽取、推理与集成。然而，当前该任务缺乏相应的数据集与方法论。本文提出LiveSum——一个基于实时解说文本生成比赛总结表格的新型基准数据集。我们在微调与零样本两种设置下评估了当前最先进LLMs在该任务上的表现，并创新性地提出名为$T^3$（Text-Tuple-Table）的流水线方法以提升其性能。大量实验结果表明，即使经过微调，LLMs仍难以胜任该任务，而我们的方法无需显式训练即可带来显著性能提升。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现有方法。我们的代码与数据可在https://github.com/HKUST-KnowComp/LiveSum-TTT获取。