Leveraging Graph to Improve Abstractive Multi-Document Summarization
Table 7: Evaluation results on the MultiNews test set with sentence-level ROUGE-L value.
['[BOLD] Model', 'R-1', 'R-2', 'R-L']
Similarly, the first block shows two popular extractive baselines, and the second block shows several strong abstractive baselines. We report the results of Lead, LexRank, PG-BRNN, HiMAP and FT following Fabbri et al. The last block shows the results of our models. The results show that our model GraphSum consistently outperforms all baselines, which further demonstrate the effectiveness of our model on different types of corpora. We also compare the performance of RoBERTa+FT and GraphSum+RoBERTa, which show that our model significantly improves all metrics.