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Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Deep learning has led to significant improvement in text summarization with various methods investigated and improved ROUGE scores reported over the years. However, gaps still exist between summaries produced by automatic summarizers and human professionals. Aiming to gain more understanding of summarization systems with respect to their strengths and limits on a fine-grained syntactic and semantic level, we consult the Multidimensional Quality Metric 1 (MQM) and quantify 8 major sources ofdoi:10.18653/v1/2020.emnlp-main.33 fatcat:xh3i4l676baq5kljtkcjmjjxna