Research map / Large language models / Language, safety and interpretability
Exploring Explainable Selection to Control Abstractive Summarization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use ESCA-BERT instead of the strongest listed baseline, BERTSUMabs | abstractive summarization · CNN/DailyMail | R-2: 19.39$^\ast$ -> 19.52 | Helped |
| Use ESCA-BERT instead of the strongest listed baseline, BERTSUMextabs | abstractive summarization · NYT50 | R-2: 31.02$^\ast$ -> 32.22 | Helped |
| Use pair-wise rather than point-wise ranking (ablation) | ESCA-BERT extractor · CNN/DailyMail extractive summaries | R-1: 32.68 -> 36.41 | Helped |
| Use pair-wise rather than point-wise ranking (ablation) | ESCA-BERT extractor · CNN/DailyMail extractive summaries | R-2: 15.41 -> 17.19 | Helped |
| Use pair-wise rather than point-wise ranking (ablation) | ESCA-BERT extractor · CNN/DailyMail extractive summaries | R-L: 30.33 -> 33.68 | Helped |
| Replace 2-layer self-attention with the interaction matrix (ablation) | ESCA-BERT extractor · CNN/DailyMail extractive summaries | R-1: 42.8 -> 42.7 | No clear effect |
| Replace 2-layer self-attention with the interaction matrix (ablation) | ESCA-BERT extractor · CNN/DailyMail extractive summaries | R-2: 20.1 -> 20.0 | No clear effect |
| Replace 2-layer self-attention with the interaction matrix (ablation) | ESCA-BERT extractor · CNN/DailyMail extractive summaries | R-L: 39.2 -> 39.2 | No clear effect |
| Set novelty threshold to ε_n=0.3 rather than ε_n=0 (ablation) | ESCA controllable inference · CNN/DailyMail synthetic novelty | R-1: 44.78 -> 45.66 | Helped |
| Set novelty threshold to ε_n=0.3 rather than ε_n=0 (ablation) | ESCA controllable inference · CNN/DailyMail synthetic novelty | R-2: 35.39 -> 36.28 | Helped |
| Set relevance threshold to ε_r=0.5 rather than ε_r=0 (ablation) | ESCA controllable inference · CNN/DailyMail synthetic relevance | R-1: 41.35 -> 41.52 | Helped |
| Set relevance threshold to ε_r=0.5 rather than ε_r=0 (ablation) | ESCA controllable inference · CNN/DailyMail synthetic relevance | R-L: 38.57 -> 38.55 | Hurt |
Closest papers by meaning
- Controlling the Amount of Verbatim Copying in Abstractive Summarization (AAAI 2020 · similarity 0.90)
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- Unsupervised Extractive Summarization with Learnable Length Control Strategies (AAAI 2024 · similarity 0.86)
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