Leveraging Graph to Improve Abstractive Multi-Document Summarization
Table 6: Evaluation results on the WikiSum test set with sentence-level ROUGE-L value.
['[BOLD] Model', 'R-1', 'R-2', 'R-L']
Several strong extractive baselines and abstractive baselines are also evaluated and compared with our models. The second block shows the results of abstractive methods: (1) FT (Flat Transformer), a transformer-based encoder-decoder model on a flat token sequence; (2) T-DMCA, the best performing model of Liu et al. ; (3) HT (Hierarchical Transformer), a model with hierarchical transformer encoder and flat transformer decoder, proposed by Liu and Lapata The last block shows the results of our models, which are feed with 30 paragraphs (about 2400 tokens) as input. The results show that all abstractive models outperform the extractive ones. Compared with FT, T-DMCA and HT, our model GraphSum achieves significant improvements on all three metrics, which demonstrates the effectiveness of our model.