Neural Text Summarization: A Critical Evaluation

Wojciech Kryscinski, Nitish Shirish Keskar, Bryan McCann, Caiming Xiong, Richard Socher
2019 Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)  
Text summarization aims at compressing long documents into a shorter form that conveys the most important parts of the original document. Despite increased interest in the community and notable research effort, progress on benchmark datasets has stagnated. We critically evaluate key ingredients of the current research setup: datasets, evaluation metrics, and models, and highlight three primary shortcomings: 1) automatically collected datasets leave the task underconstrained and may contain
more » ... detrimental to training and evaluation, 2) current evaluation protocol is weakly correlated with human judgment and does not account for important characteristics such as factual correctness, 3) models overfit to layout biases of current datasets and offer limited diversity in their outputs.
doi:10.18653/v1/d19-1051 dblp:conf/emnlp/KryscinskiKMXS19 fatcat:oex3xjc3frh5xcxz7qonuvi2gq