Papers with graph-based ranking algorithm
Bipartite Graph Pre-training for Unsupervised Extractive Summarization with Graph Convolutional Auto-Encoders (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to rank sentences using pre-trained embeddings create a gap due to different optimization objectives. |
| Approach: | They propose a pre-trained embedding process that optimizes informative sentences . they use sentence-word bipartite graphs to model intra-sentential distinctive features . |
| Outcome: | The proposed model outperforms heavy BERT- or RoBERTa-based sentence ranking methods by providing summary-worthy representations. |
Sentence Centrality Revisited for Unsupervised Summarization (P19-1)
Copied to clipboard
| Challenge: | Experimental results on three news summarization datasets representative of different languages and writing styles show that our approach outperforms strong baselines by a wide margin. |
| Approach: | They propose an unsupervised approach that uses a popular ranking algorithm to compute node centrality. |
| Outcome: | The proposed approach outperforms baselines on three news summarization datasets representative of different languages and writing styles. |