Text-Tuple-Table: Towards Information Integration in Text-to-Table Generation via Global Tuple Extraction
This work addresses a lack of datasets and methods for information integration in text-to-table generation, which is incremental as it builds on existing LLM capabilities for downstream tasks like summarization.
The paper tackles the problem of generating summary tables from real-time commentary texts, introducing a new benchmark dataset called LiveSum and a pipeline called Text-Tuple-Table (T^3) that improves performance without explicit training, achieving substantial gains over state-of-the-art LLMs which struggle even after fine-tuning.
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.