Skip to main content

Research Repository

Advanced Search

A case-based approach for content planning in data-to-text generation.

Upadhyay, Ashish; Massie, Stewart

Authors



Contributors

Mark T. Keane
Editor

Abstract

The problem of Data-to-Text Generation (D2T) is usually solved using a modular approach by breaking the generation process into some variant of planning and realisation phases. Traditional methods have been very good at producing high quality texts but are difficult to build for complex domains and also lack diversity. On the other hand, current neural systems offer scalability and diversity but at the expense of being inaccurate. Case-Based approaches try to mitigate the accuracy and diversity trade-off by providing better accuracy than neural systems and better diversity than traditional systems. However, they still fare poorly against neural systems when measured on the dimensions of content selection and diversity. In this work, a Case-Based approach for content-planning in D2T, called CBR-Plan, is proposed which selects and organises the key components required for producing a summary, based on similar previous examples. Extensive experiments are performed to demonstrate the effectiveness of the proposed method against a variety of benchmark and baseline systems, ranging from template-based, to case-based and neural systems. The experimental results indicate that CBR-Plan is able to select more relevant and diverse content than other systems.

Citation

UPADHYAY, A. and MASSIE, S. 2022. A case-based approach for content planning in data-to-text generation. In Keane, M.T. and Wiratunga, N. (eds.) Case-based reasoning research and development: proceedings of the 30th International conference on case-based reasoning (ICCBR 2022), 12-15 September 2022, Nancy, France. Lecture notes in computer science, 13405. Cham: Springer [online], pages 380-394. Available from: https://doi.org/10.1007/978-3-031-14923-8_25

Conference Name 30th International conference on case-based reasoning (ICCBR 2022)
Conference Location Nancy, France
Start Date Sep 12, 2022
End Date Sep 15, 2022
Acceptance Date Jul 22, 2022
Online Publication Date Aug 14, 2022
Publication Date Dec 31, 2022
Deposit Date Sep 27, 2022
Publicly Available Date Aug 15, 2023
Publisher Springer
Pages 380-394
Series Title Lecture notes in computer science (LNCS)
Series Number 13405
Book Title Case-based reasoning research and development: proceedings of the 30th International conference on case-based reasoning (ICCBR 2022), 12-15 September 2022, Nancy, France
ISBN 9783031149221
DOI https://doi.org/10.1007/978-3-031-14923-8_25
Keywords Data-t-text generation; Case-based planning; Content planning
Public URL https://rgu-repository.worktribe.com/output/1753224

Files

This file is under embargo until Aug 15, 2023 due to copyright reasons.

Contact publications@rgu.ac.uk to request a copy for personal use.




You might also like



Downloadable Citations