# Text-Tuple-Table: Towards Information Integration in Text-to-Table Generation via Global Tuple Extraction

arXiv: https://arxiv.org/abs/2404.14215 · HTML: https://mlsanity.com/paper/2404.14215/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 50 references that are in the map.
- First posted on arXiv 22 Apr 2024.

## Builds on (most cited first, 30 of 34)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence](https://mlsanity.com/paper/2401.14196/) (2024)
- [WizardCoder: Empowering Code Large Language Models with Evol-Instruct](https://mlsanity.com/paper/2306.08568/) (2023)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [BARTScore: Evaluating Generated Text as Text Generation](https://mlsanity.com/paper/2106.11520/) (2021)
- [Decomposed Prompting: A Modular Approach for Solving Complex Tasks](https://mlsanity.com/paper/2210.02406/) (2022)
- [HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data](https://mlsanity.com/paper/2004.07347/) (2020)
- [The E2E Dataset: New Challenges For End-to-End Generation](https://mlsanity.com/paper/1706.09254/) (2017)
- [Challenges in Data-to-Document Generation](https://mlsanity.com/paper/1707.08052/) (2017)
- [Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding](https://mlsanity.com/paper/2401.04398/) (2024)
- [Large Language Models for Generative Information Extraction: A Survey](https://mlsanity.com/paper/2312.17617/) (2023)
- [CORD-19: The COVID-19 Open Research Dataset](https://mlsanity.com/paper/2004.10706/) (2020)
- [FANToM: A Benchmark for Stress-testing Machine Theory of Mind in Interactions](https://mlsanity.com/paper/2310.15421/) (2023)
- [InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction](https://mlsanity.com/paper/2304.08085/) (2023)
- [SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization](https://mlsanity.com/paper/2212.10465/) (2022)
- [Structured Chain-of-Thought Prompting for Code Generation](https://mlsanity.com/paper/2305.06599/) (2023)
- [Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!](https://mlsanity.com/paper/2303.08559/) (2023)
- [GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction](https://mlsanity.com/paper/2310.03668/) (2023)
- [Successive Prompting for Decomposing Complex Questions](https://mlsanity.com/paper/2212.04092/) (2022)
- [PLACES: Prompting Language Models for Social Conversation Synthesis](https://mlsanity.com/paper/2302.03269/) (2023)
- [Table-GPT: Table-tuned GPT for Diverse Table Tasks](https://mlsanity.com/paper/2310.09263/) (2023)
- [Text-to-Table: A New Way of Information Extraction](https://mlsanity.com/paper/2109.02707/) (2021)
- [Table-to-Text: Describing Table Region with Natural Language](https://mlsanity.com/paper/1805.11234/) (2018)
- [CAT: A Contextualized Conceptualization and Instantiation Framework for Commonsense Reasoning](https://mlsanity.com/paper/2305.04808/) (2023)
- [TAT-LLM: A Specialized Language Model for Discrete Reasoning over Tabular and Textual Data](https://mlsanity.com/paper/2401.13223/) (2024)

## Built on by (most cited first, 30 of 33)

- [From Automation to Autonomy: A Survey on Large Language Models in Scientific Discovery](https://mlsanity.com/paper/2505.13259/) (2025)
- [Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation](https://mlsanity.com/paper/2502.05151/) (2025)
- [Evaluating and Enhancing LLMs Agent based on Theory of Mind in Guandan: A Multi-Player Cooperative Game under Imperfect Information](https://mlsanity.com/paper/2408.02559/) (2024)
- [ECon: On the Detection and Resolution of Evidence Conflicts](https://mlsanity.com/paper/2410.04068/) (2024)
- [A Survey of AI Scientists](https://mlsanity.com/paper/2510.23045/) (2025)
- [EcomScriptBench: A Multi-task Benchmark for E-commerce Script Planning via Step-wise Intention-Driven Product Association](https://mlsanity.com/paper/2505.15196/) (2025)
- [Tabular Data Understanding with LLMs: A Survey of Recent Advances and Challenges](https://mlsanity.com/paper/2508.00217/) (2025)
- [Table as a Modality for Large Language Models](https://mlsanity.com/paper/2512.00947/) (2025)
- [arXiv2Table: Toward Realistic Benchmarking and Evaluation for LLM-Based Literature-Review Table Generation](https://mlsanity.com/paper/2504.10284/) (2025)
- [Map&Make: Schema Guided Text to Table Generation](https://mlsanity.com/paper/2505.23174/) (2025)
- [Constrained Reasoning Chains for Enhancing Theory-of-Mind in Large Language Models](https://mlsanity.com/paper/2409.13490/) (2024)
- [SQUiD: Synthesizing Relational Databases from Unstructured Text](https://mlsanity.com/paper/2505.19025/) (2025)
- [StructText: A Synthetic Table-to-Text Approach for Benchmark Generation with Multi-Dimensional Evaluation](https://mlsanity.com/paper/2507.21340/) (2025)
- [KnowShiftQA: How Robust are RAG Systems when Textbook Knowledge Shifts in K-12 Education?](https://mlsanity.com/paper/2412.08985/) (2024)
- [TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering](https://mlsanity.com/paper/2509.01312/) (2025)
- [XToM: Exploring the Multilingual Theory of Mind for Large Language Models](https://mlsanity.com/paper/2506.02461/) (2025)
- [Structuring the Unstructured: A Systematic Review of Text-to-Structure Generation for Agentic AI with a Universal Evaluation Framework](https://mlsanity.com/paper/2508.12257/) (2025)
- [Evaluating Structured Decoding for Text-to-Table Generation: Evidence from Three Datasets](https://mlsanity.com/paper/2508.15910/) (2025)
- [A Case for Computing on Unstructured Data](https://mlsanity.com/paper/2509.14601/) (2025)
- [DTBench: A Synthetic Benchmark for Document-to-Table Extraction](https://mlsanity.com/paper/2602.13812/) (2026)
- [LLM driven Text-to-Table Generation through Sub-Tasks Guidance and Iterative Refinement](https://mlsanity.com/paper/2508.08653/) (2025)
- [Persona Knowledge-Aligned Prompt Tuning Method for Online Debate](https://mlsanity.com/paper/2410.04239/) (2024)
- [Beyond Isolated Dots: Benchmarking Structured Table Construction as Deep Knowledge Extraction](https://mlsanity.com/paper/2507.16271/) (2025)
- [TEN: Table Explicitization, Neurosymbolically](https://mlsanity.com/paper/2508.09324/) (2025)
- [Moneyball with LLMs: Analyzing Tabular Summarization in Sports Narratives](https://mlsanity.com/paper/2510.18173/) (2025)
- [Reflective Personalization Optimization: A Post-hoc Rewriting Framework for Black-Box Large Language Models](https://mlsanity.com/paper/2511.05286/) (2025)
- [OmniStruct: Universal Text-to-Structure Generation across Diverse Schemas](https://mlsanity.com/paper/2511.18335/) (2025)
- [Using a Human-AI Teaming Approach to Create and Curate Scientific Datasets with the SCILIRE System](https://mlsanity.com/paper/2603.12638/) (2026)
- [LIFT: Last-Mile Fine-Tuning for Table Explicitation](https://mlsanity.com/paper/2605.13424/) (2026)
- [Casual as an Anchor: Resolving Supervision Misalignment in Formality Transfer Dataset](https://mlsanity.com/paper/2605.29365/) (2026)

## Abstract (arXiv)

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 T3(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.