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

Upadhyay, Ashish; Massie, Stewart; Clogher, Sean

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

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 28th International conference on case-based reasoning research and development (ICCBR 2020)
• 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
• Peer Reviewed: Peer Reviewed
• 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