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Summarizing and Exploring Tabular Data in Conversational Search

arXiv 2005.11490 ↗First posted 23 May 2020cs.IR cs.CLcited by 19

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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.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 May 2020.

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Citing papers per year: 2021: 3; 2022: 5; 2023: 3; 2024: 6; 2025: 2.

19 papers.

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  1. 2021TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Financecited by 402
  2. 2023MultiTabQA: Generating Tabular Answers for Multi-Table Question Answeringcited by 32
  3. 2021Semi-Supervised Variational Reasoning for Medical Dialogue Generationcited by 30
  4. 2023TempTabQA: Temporal Question Answering for Semi-Structured Tablescited by 27
  5. 2024PoTable: Towards Systematic Thinking via Plan-then-Execute Stage Reasoning on Tablescited by 13
  6. 2025MMTU: A Massive Multi-Task Table Understanding and Reasoning Benchmarkcited by 13
  7. 2022Parameter-Efficient Abstractive Question Answering over Tables or Textcited by 11
  8. 2022Innovations in Neural Data-to-text Generation: A Surveycited by 10
  9. 2024QFMTS: Generating Query-Focused Summaries over Multi-Table Inputscited by 9
  10. 2024Enhancing Temporal Understanding in LLMs for Semi-structured Tablescited by 8
  11. 2021A Graph-guided Multi-round Retrieval Method for Conversational Open-domain Question Answeringcited by 7
  12. 2024Flexible and Adaptable Summarization via Expertise Separationcited by 7
7 more
  1. 2024Table-LLM-Specialist: Language Model Specialists for Tables using Iterative Generator-Validator Fine-tuningcited by 7
  2. 2022Realistic Data Augmentation Framework for Enhancing Tabular Reasoningcited by 4
  3. 2022NAPG: Non-Autoregressive Program Generation for Hybrid Tabular-Textual Question Answeringcited by 4
  4. 2022Enhancing Tabular Reasoning with Pattern Exploiting Trainingcited by 3
  5. 2023InfoSync: Information Synchronization across Multilingual Semi-structured Tablescited by 3
  6. 2024Engineering Conversational Search Systems: A Review of Applications, Architectures, and Functional Componentscited by 3
  7. 2025Evidence-Guided Schema Normalization for Temporal Tabular Reasoningcited by 0