Publication
Reliability of Human Evaluation for Text Summarization: Lessons Learned and Challenges Ahead
Neslihan Iskender; Tim Polzehl; Sebastian Möller
In: Proceedings of the Workshop on Human Evaluation of NLP Systems. Workshop on Human Evaluation of NLP Systems (HumEval-2021), located at EACL 2021, April 19, Kyiv, Ukraine, Pages 86-96, ISBN 978-1-954085-10-7, Association for Computational Linguistics, 2021.
Abstract
Only a small portion of research papers with human evaluation for text summarization provide information about the participant demographics, task design, and experiment protocol. Additionally, many researchers use human evaluation as gold standard without questioning the reliability or investigating the factors that might affect the reliability of the human evaluation. As a result, there is a lack of best practices for reliable human summarization evaluation grounded by empirical evidence. To investigate human evaluation reliability, we conduct a series of human evaluation experiments, provide an overview of participant demographics, task design, experimental set-up and compare the results from different experiments. Based on our empirical analysis, we provide guidelines to ensure the reliability of expert and non-expert evaluations, and we determine the factors that might affect the reliability of the human evaluation.