Provable Fast Greedy Compressive Summarization with Any Monotone Submodular Function (N18-1)
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| Challenge: | Submodular maximization with the greedy algorithm is an effective approach to extractive summarization. |
| Approach: | They propose a submodular maximization method that is 100 to 400 times faster than existing methods for extractive summarization. |
| Outcome: | The proposed method is 100 to 400 times faster than existing method based on integer-linear-programming formulations and achieves 95%-approximation. |
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Extractive Summarization with Text Generator (2024.naacl-long)
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| Challenge: | Existing extractive systems lack gold training signals, thereby hindering learning of extractive models. |
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MSMO: Multimodal Summarization with Multimodal Output (D18-1)
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Title-based Extractive Summarization via MRC Framework (2024.lrec-main)
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