DynE: Dynamic Ensemble Decoding for Multi-Document Summarization
Table 2: Evaluation results on WCEP test set.
['[BOLD] F-score Method', '[BOLD] F-score R1', '[BOLD] F-score R2', '[BOLD] F-score RL']
Our ensembling method excels on this dataset, likely because it is highly abstractive, with short reference summaries. Fine-tuning the base model leads to significant improvement, and adding more documents to input clusters consistently increases summary quality. An ensemble with five documents per input cluster outperforms all extractive baselines. Note that the truncated version of WCEP contains clusters of up to 100 documents, but we use only up to five randomly selected documents from each cluster. Thus the performance ceiling for abstractive MDS on this dataset is potentially much higher.