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Found 8 papers, 3 papers with code
Studying the Impact of Filling Information Gaps on the Output Quality of Neural Data-to-Text
It is unfair to expect neural data-to-text to produce high quality output when there are gaps between system input data and information contained in the training text.
Shared Task on Evaluating Accuracy
We propose a shared task on methodologies and algorithms for evaluating the accuracy of generated texts, specifically summaries of basketball games produced from basketball box score and other game data.
Generation Challenges: Results of the Accuracy Evaluation Shared Task
The Shared Task on Evaluating Accuracy focused on techniques (both manual and automatic) for evaluating the factual accuracy of texts produced by neural NLG systems, in a sports-reporting domain.
Underreporting of errors in NLG output, and what to do about it
We observe a severe under-reporting of the different kinds of errors that Natural Language Generation systems make.
A Gold Standard Methodology for Evaluating Accuracy in Data-To-Text Systems
Most Natural Language Generation systems need to produce accurate texts.
SportSett:Basketball - A robust and maintainable dataset for Natural Language Generation
In this resource paper, we introduce the SportSett:Basketball database.
Shared Task on Evaluating Accuracy in Natural Language Generation
We propose a shared task on methodologies and algorithms for evaluating the accuracy of generated texts.
Comprehension Driven Document Planning in Natural Language Generation Systems
This paper proposes an approach to NLG system design which focuses on generating output text which can be more easily processed by the reader.
