# Table-to-Text Generation with Effective Hierarchical Encoder on Three Dimensions (Row, Column and Time)

arXiv: https://arxiv.org/abs/1909.02304 · HTML: https://mlsanity.com/paper/1909.02304/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2019.

## Built on by (most cited first, 18 of 18)

- [Data-to-text Generation with Macro Planning](https://mlsanity.com/paper/2102.02723/) (2021)
- [Data-to-text Generation with Variational Sequential Planning](https://mlsanity.com/paper/2202.13756/) (2022)
- [Recent Advances in Neural Text Generation: A Task-Agnostic Survey](https://mlsanity.com/paper/2203.03047/) (2022)
- [Stage-wise Fine-tuning for Graph-to-Text Generation](https://mlsanity.com/paper/2105.08021/) (2021)
- [Innovations in Neural Data-to-text Generation: A Survey](https://mlsanity.com/paper/2207.12571/) (2022)
- [Generation Challenges: Results of the Accuracy Evaluation Shared Task](https://mlsanity.com/paper/2108.05644/) (2021)
- [Synthetic SQL Column Descriptions and Their Impact on Text-to-SQL Performance](https://mlsanity.com/paper/2408.04691/) (2024)
- [LOGEN: Few-shot Logical Knowledge-Conditioned Text Generation with Self-training](https://mlsanity.com/paper/2112.01404/) (2021)
- [Columbo: Expanding Abbreviated Column Names for Tabular Data Using Large Language Models](https://mlsanity.com/paper/2508.09403/) (2025)
- [Towards Table-to-Text Generation with Pretrained Language Model: A Table Structure Understanding and Text Deliberating Approach](https://mlsanity.com/paper/2301.02071/) (2023)
- [A Systematic Review of Data-to-Text NLG](https://mlsanity.com/paper/2402.08496/) (2024)
- [Plan-then-Seam: Towards Efficient Table-to-Text Generation](https://mlsanity.com/paper/2302.05138/) (2023)
- [DISK: Domain-constrained Instance Sketch for Math Word Problem Generation](https://mlsanity.com/paper/2204.04686/) (2022)
- [Impact of Model Size on Fine-tuned LLM Performance in Data-to-Text Generation: A State-of-the-Art Investigation](https://mlsanity.com/paper/2407.14088/) (2024)
- [TACO: Task-Aware Column Description Generation Using LLMs](https://mlsanity.com/paper/2606.21685/) (2026)
- [Data Transformation Strategies to Remove Heterogeneity](https://mlsanity.com/paper/2507.12677/) (2025)
- [Towards Improving Interpretability of Language Model Generation through a Structured Knowledge Discovery Approach](https://mlsanity.com/paper/2511.23335/) (2025)
- [Frame-Guided Synthetic Claim Generation for Automatic Fact-Checking Using High-Volume Tabular Data](https://mlsanity.com/paper/2601.17232/) (2026)

## Abstract (arXiv)

Although Seq2Seq models for table-to-text generation have achieved remarkable progress, modeling table representation in one dimension is inadequate. This is because (1) the table consists of multiple rows and columns, which means that encoding a table should not depend only on one dimensional sequence or set of records and (2) most of the tables are time series data (e.g. NBA game data, stock market data), which means that the description of the current table may be affected by its historical data. To address aforementioned problems, not only do we model each table cell considering other records in the same row, we also enrich table's representation by modeling each table cell in context of other cells in the same column or with historical (time dimension) data respectively. In addition, we develop a table cell fusion gate to combine representations from row, column and time dimension into one dense vector according to the saliency of each dimension's representation. We evaluated our methods on ROTOWIRE, a benchmark dataset of NBA basketball games. Both automatic and human evaluation results demonstrate the effectiveness of our model with improvement of 2.66 in BLEU over the strong baseline and outperformance of state-of-the-art model.