# A Sequence-to-Sequence&Set Model for Text-to-Table Generation

arXiv: https://arxiv.org/abs/2306.00137 · HTML: https://mlsanity.com/paper/2306.00137/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 34 references that are in the map.
- First posted on arXiv 31 May 2023.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity](https://mlsanity.com/paper/2104.08786/) (2021)
- [ToTTo: A Controlled Table-To-Text Generation Dataset](https://mlsanity.com/paper/2004.14373/) (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)
- [Unified Structure Generation for Universal Information Extraction](https://mlsanity.com/paper/2203.12277/) (2022)
- [Structured Prediction as Translation between Augmented Natural Languages](https://mlsanity.com/paper/2101.05779/) (2021)
- [Document-Level Event Argument Extraction by Conditional Generation](https://mlsanity.com/paper/2104.05919/) (2021)
- [A Unified Generative Framework for Various NER Subtasks](https://mlsanity.com/paper/2106.01223/) (2021)
- [Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction](https://mlsanity.com/paper/2106.09232/) (2021)
- [Neural Machine Translation with Byte-Level Subwords](https://mlsanity.com/paper/1909.03341/) (2019)
- [Logical Natural Language Generation from Open-Domain Tables](https://mlsanity.com/paper/2004.10404/) (2020)
- [Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction](https://mlsanity.com/paper/1911.09886/) (2019)
- [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)
- [One2Set: Generating Diverse Keyphrases as a Set](https://mlsanity.com/paper/2105.11134/) (2021)
- [A Sequence-to-Set Network for Nested Named Entity Recognition](https://mlsanity.com/paper/2105.08901/) (2021)
- [Asynchronous Bidirectional Decoding for Neural Machine Translation](https://mlsanity.com/paper/1801.05122/) (2018)
- [WR-ONE2SET: Towards Well-Calibrated Keyphrase Generation](https://mlsanity.com/paper/2211.06862/) (2022)

## Built on by (most cited first, 16 of 16)

- [A Survey of Text Watermarking in the Era of Large Language Models](https://mlsanity.com/paper/2312.07913/) (2023)
- [StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization](https://mlsanity.com/paper/2410.08815/) (2024)
- [Text-Tuple-Table: Towards Information Integration in Text-to-Table Generation via Global Tuple Extraction](https://mlsanity.com/paper/2404.14215/) (2024)
- [Struc-Bench: Are Large Language Models Really Good at Generating Complex Structured Data?](https://mlsanity.com/paper/2309.08963/) (2023)
- [Structsum Generation for Faster Text Comprehension](https://mlsanity.com/paper/2401.06837/) (2024)
- [Is This a Bad Table? A Closer Look at the Evaluation of Table Generation from Text](https://mlsanity.com/paper/2406.14829/) (2024)
- [DocTabQA: Answering Questions from Long Documents Using Tables](https://mlsanity.com/paper/2408.11490/) (2024)
- [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)
- [Finding What Matters: Anchoring Context Knowledge with Evolving Indices for Iterative Retrieval](https://mlsanity.com/paper/2601.16462/) (2026)
- [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)
- [CT-Eval: Benchmarking Chinese Text-to-Table Performance in Large Language Models](https://mlsanity.com/paper/2405.12174/) (2024)
- [Confidence-Aware Sub-Structure Beam Search (CABS): Mitigating Hallucination in Structured Data Generation with Large Language Models](https://mlsanity.com/paper/2406.00069/) (2024)
- [LLM driven Text-to-Table Generation through Sub-Tasks Guidance and Iterative Refinement](https://mlsanity.com/paper/2508.08653/) (2025)
- [Moneyball with LLMs: Analyzing Tabular Summarization in Sports Narratives](https://mlsanity.com/paper/2510.18173/) (2025)
- [TEAR: Table Extraction with Attribute Recommendation from Texts via Large Language Models](https://mlsanity.com/paper/2609.15205/) (2026)

## Abstract (arXiv)

Recently, the text-to-table generation task has attracted increasing attention due to its wide applications. In this aspect, the dominant model formalizes this task as a sequence-to-sequence generation task and serializes each table into a token sequence during training by concatenating all rows in a top-down order. However, it suffers from two serious defects: 1) the predefined order introduces a wrong bias during training, which highly penalizes shifts in the order between rows; 2) the error propagation problem becomes serious when the model outputs a long token sequence. In this paper, we first conduct a preliminary study to demonstrate the generation of most rows is order-insensitive. Furthermore, we propose a novel sequence-to-sequence&set text-to-table generation model. Specifically, in addition to a text encoder encoding the input text, our model is equipped with a table header generator to first output a table header, i.e., the first row of the table, in the manner of sequence generation. Then we use a table body generator with learnable row embeddings and column embeddings to generate a set of table body rows in parallel. Particularly, to deal with the issue that there is no correspondence between each generated table body row and target during training, we propose a target assignment strategy based on the bipartite matching between the first cells of generated table body rows and targets. Experiment results show that our model significantly surpasses the baselines, achieving state-of-the-art performance on commonly-used datasets.