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Case-based approach to automated natural language generation for obituaries.

Upadhyay, Ashish; Massie, Stewart; Clogher, Sean

Authors

Sean Clogher



Abstract

Automated generation of human readable text from structured information is challenging because grammatical rules are complex making good quality outputs difficult to achieve. Textual Case-Based Reasoning provides one approach in which the text from previously solved examples with similar inputs is reused as a template solution to generate text for the current problem. Natural Language Generation also poses a challenge when evaluating the quality of the text generated due to the high cost of human labelling and the variety in potential good quality solutions. In this paper, we propose two case-based approaches for reusing text to automatically generate an obituary from a set of input attribute-value pairs. The case-base is acquired by crawling and then tagging existing solutions published on the web to create cases as problem-solution pairs. We evaluate the quality of the text generation system with a novel unsupervised case alignment metric using normalised discounted cumulative gain which is compared to a supervised approach and human evaluation. Initial results show that our proposed evaluation measure is effective and correlates well with average attribute error evaluation which is a crude surrogate to human feedback. The system is being deployed in a real-world application with a startup company in Aberdeen to produce automated obituaries.

Citation

UPADHYAY, A., MASSIE, S. and CLOGHER, S. 2020. Case-based approach to automated natural language generation for obituaries. In Watson, I. and Weber, R. (eds.) Case-based reasoning research and development: proceedings of the 28th International conference on case-based reasoning research and development (ICCBR 2020), 8-12 June 2020, Salamanca, Spain [virtual conference]. Lecture notes in computer science, 12311. Cham: Springer [online], pages 279-294. Available from: https://doi.org/10.1007/978-3-030-58342-2_18

Conference Name 28th International conference on case-based reasoning research and development (ICCBR 2020)
Conference Location [virtual conference]
Start Date Jun 8, 2020
End Date Jun 12, 2020
Acceptance Date Apr 14, 2020
Online Publication Date Oct 3, 2020
Publication Date Oct 31, 2020
Deposit Date Nov 16, 2020
Publicly Available Date Nov 16, 2020
Publisher Springer
Pages 279-294
Series Title Lecture notes in computer science
Series Number 12311
Series ISSN 1611-3349
Book Title Case-based reasoning research and development: proceedings of the 28th International conference on case-based reasoning research and development (ICCBR 2020), 8-12 June 2020, Salamanca, Spain.
ISBN 9783030583415
DOI https://doi.org/10.1007/978-3-030-58342-2_18
Keywords Natural language generation; Textual case-based reasoning; Text evaluation
Public URL https://rgu-repository.worktribe.com/output/979052

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