@misc { , title = {A case-based approach to data-to-text generation. [Software]}, abstract = {Traditional Data-to-Text Generation (D2T) systems utilise carefully crafted domain specific rules and templates to generate high quality accurate texts. More recent approaches use neural systems to learn domain rules from the training data to produce very fluent and diverse texts. However, there is a trade-off with rule-based systems producing accurate text but that may lack variation, while learning-based systems produce more diverse texts but often with poorer accuracy. This code has been used to help propose a case-based approach for D2T, which mitigates the impact of this trade-off by dynamically selecting templates from the training corpora. In our approach we develop a novel case-alignment based, feature weighing method that is used to build an effective similarity measure. Extensive experimentation is performed on a sports domain dataset. Through Extractive Evaluation metrics, we demonstrate the benefit of the CBR system over a rule-based baseline and a neural benchmark. The file accompanying this OpenAIR record contains a link to where the code is held on GitHub. The GitHub repository also includes information on how to use the code.}, note = {INFO INCOMPLETE (Info via Scopus alert 7/10/2021 LM) PERMISSION GRANTED (version = VOR; embargo = none; licence = Pub's own 2/11/2021 LM) DOCUMENT NOT REQUIRED (link-only record requested by contact 25/10/2021 LM) ADDITIONAL INFO - Contact: Ashish Upadhyay; Stewart Massie}, publicationstatus = {Unpublished}, url = {https://rgu-repository.worktribe.com/output/1512837}, keyword = {Case-based reasoning (CBR), Natural language generation (NLG), Natural language processing, Data-to-text, Textual case-based reasoning (Textual CBR), Feature weighting}, author = {Upadhyay, Ashish and Massie, Stewart and Singh, Ritwik Kumar and Gupta, Garima and Ojha, Muneendra} }