# Summarizing and Exploring Tabular Data in Conversational Search

arXiv: https://arxiv.org/abs/2005.11490 · HTML: https://mlsanity.com/paper/2005.11490/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2020.

## Built on by (most cited first, 19 of 19)

- [TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance](https://mlsanity.com/paper/2105.07624/) (2021)
- [MultiTabQA: Generating Tabular Answers for Multi-Table Question Answering](https://mlsanity.com/paper/2305.12820/) (2023)
- [Semi-Supervised Variational Reasoning for Medical Dialogue Generation](https://mlsanity.com/paper/2105.06071/) (2021)
- [TempTabQA: Temporal Question Answering for Semi-Structured Tables](https://mlsanity.com/paper/2311.08002/) (2023)
- [PoTable: Towards Systematic Thinking via Plan-then-Execute Stage Reasoning on Tables](https://mlsanity.com/paper/2412.04272/) (2024)
- [MMTU: A Massive Multi-Task Table Understanding and Reasoning Benchmark](https://mlsanity.com/paper/2506.05587/) (2025)
- [Parameter-Efficient Abstractive Question Answering over Tables or Text](https://mlsanity.com/paper/2204.03357/) (2022)
- [Innovations in Neural Data-to-text Generation: A Survey](https://mlsanity.com/paper/2207.12571/) (2022)
- [QFMTS: Generating Query-Focused Summaries over Multi-Table Inputs](https://mlsanity.com/paper/2405.05109/) (2024)
- [Enhancing Temporal Understanding in LLMs for Semi-structured Tables](https://mlsanity.com/paper/2407.16030/) (2024)
- [Table-LLM-Specialist: Language Model Specialists for Tables using Iterative Generator-Validator Fine-tuning](https://mlsanity.com/paper/2410.12164/) (2024)
- [A Graph-guided Multi-round Retrieval Method for Conversational Open-domain Question Answering](https://mlsanity.com/paper/2104.08443/) (2021)
- [Flexible and Adaptable Summarization via Expertise Separation](https://mlsanity.com/paper/2406.05360/) (2024)
- [Realistic Data Augmentation Framework for Enhancing Tabular Reasoning](https://mlsanity.com/paper/2210.12795/) (2022)
- [NAPG: Non-Autoregressive Program Generation for Hybrid Tabular-Textual Question Answering](https://mlsanity.com/paper/2211.03462/) (2022)
- [Enhancing Tabular Reasoning with Pattern Exploiting Training](https://mlsanity.com/paper/2210.12259/) (2022)
- [InfoSync: Information Synchronization across Multilingual Semi-structured Tables](https://mlsanity.com/paper/2307.03313/) (2023)
- [Engineering Conversational Search Systems: A Review of Applications, Architectures, and Functional Components](https://mlsanity.com/paper/2407.00997/) (2024)
- [Evidence-Guided Schema Normalization for Temporal Tabular Reasoning](https://mlsanity.com/paper/2512.00329/) (2025)

## Abstract (arXiv)

Tabular data provide answers to a significant portion of search queries. However, reciting an entire result table is impractical in conversational search systems. We propose to generate natural language summaries as answers to describe the complex information contained in a table. Through crowdsourcing experiments, we build a new conversation-oriented, open-domain table summarization dataset. It includes annotated table summaries, which not only answer questions but also help people explore other information in the table. We utilize this dataset to develop automatic table summarization systems as SOTA baselines. Based on the experimental results, we identify challenges and point out future research directions that this resource will support.