Map&Make: Schema Guided Text to Table Generation

Naman Ahuja1*, Fenil Bardoliya1*, Chitta Baral1, Vivek Gupta1
1Arizona State University
*Equal contribution

ACL 2025 (Main)

arXiv Code

Abstract

Transforming dense, detailed, unstructured text into an interpretable and summarised table, also colloquially known as Text-to-Table generation, is an essential task for information retrieval. Current methods, however, miss out on how and what complex information to extract; they also lack the ability to infer data from the text. In this paper, we introduce a versatile approach, Map&Make, which ''dissects'' text into propositional atomic statements. This facilitates granular decomposition to extract the latent schema. The schema is then used to populate the tables that capture the qualitative nuances and the quantitative facts in the original text. Our approach is tested against two challenging datasets, Rotowire, renowned for complex and multi-table schema, and Livesum, which demands numerical aggregation. By carefully identifying and correcting hallucination errors in Rotowire, we aim to achieve a cleaner and more reliable benchmark. We evaluate our method rigorously on a comprehensive suite of comparative and referenceless metrics. Our findings demonstrate significant improvement results across both datasets with better interpretability in Text-to-Table generation. Moreover, through detailed ablation studies and analyses, we investigate the factors contributing to superior performance and validate the practicality of our framework in structured summarization tasks.

Framework

M&M Framework

Results

Rotowire

Performance comparison of different methods on Rotowire across models using various string similarity metrics


Performance comparison using TabEval and AutoQA on Rotowire across strategies on various models.


Livesum


Performance comparison using string similarity metrics across different categories showing Error Rates (in %) and RMSE scores of GPT-4o and Gemini-2.0-flash-exp for Livesum.

Analysis


Average Per-Table Error Counts Across Rows and Columns for Rotowire


RMSE of Overcounting and Undercounting Instances for Livesum. Uncercounted refers to cell values less than the ground truth, Overcounted refers to cell values more than the ground truths.


Comparison of Schema-Coverage with Increasing Table Sizes for Rotowire.

BibTeX

@inproceedings{ahuja-etal-2025-map,
    title = "Map{\&}Make: Schema Guided Text to Table Generation",
    author = "Ahuja, Naman  and
      Bardoliya, Fenil  and
      Baral, Chitta  and
      Gupta, Vivek",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.acl-long.1460/",
    pages = "30249--30262",
    ISBN = "979-8-89176-251-0",
    abstract = "Transforming dense, unstructured text into interpretable tables{---}commonly referred to as Text-to-Table generation{---}is a key task in information extraction. Existing methods often overlook what complex information to extract and how to infer it from text. We present Map{\&}Make, a versatile approach that decomposes text into atomic propositions to infer latent schemas, which are then used to generate tables capturing both qualitative nuances and quantitative facts. We evaluate our method on three challenging datasets: Rotowire, known for its complex, multi-table schema; Livesum which requires numerical aggregation; and Wiki40 which require open text extraction from mulitple domains. By correcting hallucination errors in Rotowire, we also provide a cleaner benchmark. Our method shows significant gains in both accuracy and interpretability across comprehensive comparative and referenceless metrics. Finally, ablation studies highlight the key factors driving performance and validate the utility of our approach in structured summarization. Code and data are available at: https://coral-lab-asu.github.io/map-make."
} 
}