Learning to Create Sentence Semantic Relation Graphs for Multi-Document Summarization
Table 2: Comparison of various models using ROUGE-1/2 on DUC 2004 with 100 words summaries.
['Model', 'ROUGE-1', 'ROUGE-2']
We use as baselines FreqSum Nenkova et al. LexRank Erkan and Radev ; Centroid Radev et al. ; CLASSY04 Conroy et al. CLASSY11 Conroy et al. All of these models are significantly underperforming compared to SemSentSum. In addition, we include state-of-the-art models: RegSum Hong and Nenkova We outperform both in terms of ROUGE-1. For ROUGE-2 scores we achieve better results than GCN+PADG but without any use of domain-specific hand-crafted features and a much smaller and simpler model. Finally, RegSum achieves a similar ROUGE-2 score but computes sentence saliences based on word scores, incorporating a rich set of word-level and domain-specific features. Nonetheless, our model is competitive and does not depend on hand-crafted features due to its full data-driven nature and thus, it is not limited to a single domain.