Research map / Learning theory and algorithms / Submodular and game algorithms
Online Summarization via Submodular and Convex Optimization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use incremental online selection instead of the corresponding offline method | Convex summarization (p=∞) · SumMe | MCC: 0.00869 -> 0.0230 | Helped |
| Use incremental online selection instead of the corresponding offline method | Convex summarization (p=∞) · TVSum | MCC: 0.0159 -> 0.02303 | Helped |
| Use incremental online selection instead of the corresponding offline method | Convex summarization (p=2) · SumMe | MCC: -0.0273 -> 0.0291 | Helped |
| Use incremental online selection instead of the corresponding offline method | Convex summarization (p=2) · TVSum | MCC: 0.00524 -> -0.00357 | Hurt |
| Use incremental online selection instead of the corresponding offline method | Greedy submodular summarization · SumMe | MCC: 0.0263 -> 0.0142 | Hurt |
| Use incremental online selection instead of the corresponding offline method | Greedy submodular summarization · TVSum | MCC: 0.005016 -> 0.02446 | Helped |
| Use incremental online selection instead of selecting representatives independently per batch | Convex summarization (p=∞) · SumMe | MCC: -0.0211 -> 0.0230 | Helped |
| Use incremental online selection instead of selecting representatives independently per batch | Convex summarization (p=∞) · TVSum | MCC: 0.00055 -> 0.02303 | Helped |
| Use incremental online selection instead of selecting representatives independently per batch | Convex summarization (p=2) · SumMe | MCC: -0.0282 -> 0.0291 | Helped |
| Use incremental online selection instead of selecting representatives independently per batch | Convex summarization (p=2) · TVSum | MCC: -0.00777 -> -0.00357 | Helped |
| Use incremental online selection instead of selecting representatives independently per batch | Greedy submodular summarization · SumMe | MCC: -0.0071 -> 0.0142 | Helped |
| Use incremental online selection instead of selecting representatives independently per batch | Greedy submodular summarization · TVSum | MCC: 0.007184 -> 0.02446 | Helped |
Closest papers by meaning
- Subset Selection and Summarization in Sequential Data (NeurIPS 2017 · similarity 0.89)
- Data Summarization at Scale: A Two-Stage Submodular Approach (ICML 2018 · similarity 0.88)
- Streaming Non-Monotone Submodular Maximization: Personalized Video Summarization on the Fly (AAAI 2018 · similarity 0.88)
- Multinoulli Extension: A Lossless Yet Effective Probabilistic Framework for Subset Selection over Partition Constraints (ICML 2025 · similarity 0.85)
- Online and Streaming Algorithms for Constrained k-Submodular Maximization (AAAI 2025 · similarity 0.85)
- Online Two-Stage Submodular Maximization (NeurIPS 2025 · similarity 0.84)
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