DynE: Dynamic Ensemble Decoding for Multi-Document Summarization
Table 4: Evaluation results on DUC2004 MDS test set, compared with Lebanoff et al 2018.
['[BOLD] F-score Method', '[BOLD] F-score R1', '[BOLD] F-score R2', '[BOLD] F-score R-SU']
For this dataset, we cannot fine tune because we do not have a held-out training dataset. Despite this, performance improves as we add more documents to input clusters, and approaches the results of strong abstractive baselines.