short-paper

Summarizing and Exploring Tabular Data in Conversational Search

Authors:

Shuo Zhang,

Zhuyun Dai,

Krisztian Balog,

Jamie CallanAuthors Info & Claims

SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 1537 - 1540

https://doi.org/10.1145/3397271.3401205

Published: 25 July 2020 Publication History

Get Access

Abstract

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.

Supplementary Material

MP4 File (3397271.3401205.mp4)

Tabular data provide answers to a significant portion of search queries. However, reciting an entire result table is impractical in conversational search systems.\r\nWe propose to generate natural language summaries as answers to describe the complex information contained in a table.\r\nThrough 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. \r\nWe utilize this dataset to develop automatic table summarization systems as SOTA baselines. \r\nBased on the experimental results, we identify challenges and point out future research directions that this resource will support.

Download
12.15 MB

References

[1]

Jieying Chen, Jia-Yu Pan, Christos Faloutsos, and Spiros Papadimitriou. 2013. TSum: Fast, Principled Table Summarization. In Proc. of ADKDD '13.

Digital Library

Google Scholar

[2]

Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson F. Liu, Matthew Peters, Michael Schmitz, and Luke Zettlemoyer. 2018. AllenNLP: A Deep Semantic Natural Language Processing Platform. In Proc. of Workshop for NLP Open Source Software (NLP-OSS).

Crossref

Google Scholar

[3]

Jiatao Gu, Zhengdong Lu, Hang Li, and Victor O.K. Li. 2016. Incorporating Copying Mechanism in Sequence-to-Sequence Learning. In Proc. of ACL '16.

Google Scholar

[4]

Braden Hancock, Hongrae Lee, and Cong Yu. 2019. Generating Titles for Web Tables. In Proc. of WWW '19.

Digital Library

Google Scholar

[5]

Dino Ienco, Yoann Pitarch, Pascal Poncelet, and Maguelonne Teisseire. 2013. Knowledge-Free Table Summarization. In Proc. of DWKD '13.

Digital Library

Google Scholar

[6]

Parag Jain, Anirban Laha, Karthik Sankaranarayanan, Preksha Nema, Mitesh M. Khapra, and Shreyas Shetty. 2018. A Mixed Hierarchical Attention Based Encoder-Decoder Approach for Standard Table Summarization. In Proc. of NCACL '18.

Crossref

Google Scholar

[7]

Ming-Ling Lo, Kun-Lung Wu, and Philip S. Yu. 2000. TabSum: A Flexible and Dynamic Table Summarization Approach. In Proc. ICDCS '00.

Google Scholar

[8]

Panupong Pasupat and Percy Liang. 2015. Compositional Semantic Parsing on Semi-Structured Tables. In Proc. of ACL-IJCNLP '15.

Crossref

Google Scholar

[9]

Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language Models are Unsupervised Multitask Learners (Preprint).

Google Scholar

[10]

Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2019. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. arxiv: 1910.10683

Google Scholar

[11]

K. Selccuk Candan, Huiping Cao, Yan Qi, and Maria Luisa Sapino. 2009. AlphaSum: Size-Constrained Table Summarization Using Value Lattices. In Proc. of EDBT'09.

Digital Library

Google Scholar

[12]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is All You Need. In Proc. of NIPS '17.

Google Scholar

[13]

Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, and Jamie Brew. 2019. HuggingFace's Transformers: State-of-the-art Natural Language Processing. arxiv: 1910.03771

Google Scholar

[14]

Shuo Zhang and Krisztian Balog. 2020. Web Table Extraction, Retrieval, and Augmentation: A Survey. ACM Trans. Intell. Syst. Technol. (2020).

Google Scholar

Cited By

View all

Sharma MGogineni ARamakrishnan N(2024)Neural Methods for Data-to-text GenerationACM Transactions on Intelligent Systems and Technology10.1145/366063915:5(1-46)Online publication date: 8-May-2024
https://dl.acm.org/doi/10.1145/3660639
Huang JChen HYu FLu W(2024)From Detection to Application: Recent Advances in Understanding Scientific Tables and FiguresACM Computing Surveys10.1145/365728556:10(1-39)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3657285
Li PHe YYashar DCui WGe SZhang HRifinski Fainman DZhang DChaudhuri S(2024)Table-GPT: Table Fine-tuned GPT for Diverse Table TasksProceedings of the ACM on Management of Data10.1145/36549792:3(1-28)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654979
Show More Cited By

Index Terms

Summarizing and Exploring Tabular Data in Conversational Search
1. Information systems
  1. Information retrieval
    1. Specialized information retrieval
      1. Environment-specific retrieval

Recommendations

Exploring Intelligent Functionalities of Spoken Conversational Search Systems
Open-Domain Conversational Search Assistant with Transformers
Advances in Information Retrieval
Abstract
Open-domain conversational search assistants aim at answering user questions about open topics in a conversational manner. In this paper we show how the Transformer architecture [30] achieves state-of-the-art results in key IR tasks, leveraging ...
Summarising News with Texts and Pictures
SKG '14: Proceedings of the 2014 10th International Conference on Semantics, Knowledge and Grids

As the information explosion is becoming more and more seriously, effective and efficient multi-document summarization techniques are becoming more and more necessary. Previous document summarization approaches mainly focus on texts. The poor ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2020

2548 pages

ISBN:9781450380164

DOI:10.1145/3397271

General Chairs:
Jimmy Huang
York University, Canada
,
Yi Chang
Jilin University, China
,
Xueqi Cheng
Chinese Academy of Sciences, China
,
Program Chairs:
Jaap Kamps
University of Amsterdam, Netherlands
,
Vanessa Murdock
Amazon, U.S.A.
,
Ji-Rong Wen
Renmin University of China, China
,
Yiqun Liu
Tsinghua University, China

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 July 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Funding Sources

National Science Foundation (NSF) grant IIS-1815528

Conference

SIGIR '20

Sponsor:

SIGIR

SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval

July 25 - 30, 2020

Virtual Event, China

Acceptance Rates

Overall Acceptance Rate 792 of 3,983 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

11
Total Citations
View Citations
356
Total Downloads

Downloads (Last 12 months)49
Downloads (Last 6 weeks)4

Reflects downloads up to 03 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Sharma MGogineni ARamakrishnan N(2024)Neural Methods for Data-to-text GenerationACM Transactions on Intelligent Systems and Technology10.1145/366063915:5(1-46)Online publication date: 8-May-2024
https://dl.acm.org/doi/10.1145/3660639
Huang JChen HYu FLu W(2024)From Detection to Application: Recent Advances in Understanding Scientific Tables and FiguresACM Computing Surveys10.1145/365728556:10(1-39)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3657285
Li PHe YYashar DCui WGe SZhang HRifinski Fainman DZhang DChaudhuri S(2024)Table-GPT: Table Fine-tuned GPT for Diverse Table TasksProceedings of the ACM on Management of Data10.1145/36549792:3(1-28)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654979
Chen XLi MGao SCheng XZhu QYan RGao XZhang XHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Flexible and Adaptable Summarization via Expertise SeparationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657789(2018-2027)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657789
Ghosh SGhosh SShah CKlein MBen-David AJäschke RKelly M(2024)Toward Connecting Speech Acts and Search Actions in Conversational Search TasksProceedings of the 2023 ACM/IEEE Joint Conference on Digital Libraries10.1109/JCDL57899.2023.00027(119-131)Online publication date: 26-Jun-2024
https://dl.acm.org/doi/10.1109/JCDL57899.2023.00027
Mo XXiang YPan YHou YLuo P(2024)MGCoT: Multi-Grained Contextual Transformer for table-based text generationExpert Systems with Applications10.1016/j.eswa.2024.123742250(123742)Online publication date: Sep-2024
https://doi.org/10.1016/j.eswa.2024.123742
Chen QGao XGuo XWang S(2023)Multi-head attention based candidate segment selection in QA over hybrid dataIntelligent Data Analysis10.3233/IDA-22703227:6(1839-1852)Online publication date: 20-Nov-2023
https://doi.org/10.3233/IDA-227032
Shigarov A(2022)Table understanding: Problem overviewWIREs Data Mining and Knowledge Discovery10.1002/widm.148213:1Online publication date: 21-Nov-2022
https://doi.org/10.1002/widm.1482
Li DRen ZRen PChen ZFan MMa Jde Rijke MDiaz FShah CSuel TCastells PJones RSakai T(2021)Semi-Supervised Variational Reasoning for Medical Dialogue GenerationProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3462921(544-554)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3462921
Deldjoo YTrippas JZamani HDiaz FShah CSuel TCastells PJones RSakai T(2021)Towards Multi-Modal Conversational Information SeekingProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3462806(1577-1587)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3462806
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Exploring Intelligent Functionalities of Spoken Conversational Search Systems

Open-Domain Conversational Search Assistant with Transformers

Summarising News with Texts and Pictures