  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.
