Papers with DPP
Multi-Document Summarization with Determinantal Point Processes and Contextualized Representations (D19-54)
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| Challenge: | Determinantal point processes (DPP) is one of the best performing techniques for extractive summarization. |
| Approach: | They propose to combine determinantal point processes with surface indicators for effective identification of summary-worthy sentences. |
| Outcome: | The determinantal point processes (DPP) framework is one of the best performing in summarization competitions. |
Defensive Prompt Patch: A Robust and Generalizable Defense of Large Language Models against Jailbreak Attacks (2025.findings-acl)
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| Challenge: | Recent advances in large language models (LLMs) have showcased their ability to understand and generate text akin to human interaction. |
| Approach: | They propose a prompt-based defense mechanism specifically designed to protect LLMs against jailbreak attacks by introducing jailbreak prompts into malicious queries. |
| Outcome: | Empirical results show that the proposed defense outperforms existing defense strategies in balancing safety and utility while maintaining high utility. |
LeCoPCR: Legal Concept-guided Prior Case Retrieval for European Court of Human Rights cases (2025.findings-naacl)
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| Challenge: | Existing approaches overlook the underlying semantic intent in determining relevance with respect to a query case. |
| Approach: | They propose a method that generates intents in the form of legal concepts from a query case facts and then augments the query with these concepts to enhance models understanding of semantic intent. |
| Outcome: | The proposed approach generates intents in the form of legal concepts and augments the query with these concepts to enhance models understanding of semantic intent that dictates relavance. |
Improving the Similarity Measure of Determinantal Point Processes for Extractive Multi-Document Summarization (P19-1)
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| Challenge: | Despite the empirical success of multi-document summarization, most datasets remain small and the cost of hiring hu-1 is prohibitive. |
| Approach: | They propose a novel method for extractive multi-document summarization that measures redundancy between a pair of sentences based on surface form and semantic information. |
| Outcome: | The proposed method outperforms baseline methods on benchmark datasets and is particularly useful for documents created by multiple authors containing redundant yet lexically diverse expressions. |
Representative Demonstration Selection for In-Context Learning with Two-Stage Determinantal Point Process (2023.emnlp-main)
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| Challenge: | Existing methods tend to select different demonstrations for each test instance, which is time-consuming and poses limitations in practical scenarios. |
| Approach: | They propose to select a representative subset of in-context demonstrations that can prompt different test instances in a specific task. |
| Outcome: | The proposed method can be used to generate representative in-context demonstrations. |
CrisPrune: Combining Contextual Relevance and Intrinsic Saliency for Efficient Visual Token Pruning in MLLMs (2026.findings-acl)
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| Challenge: | Existing methods for visual token pruning compromise the integrity of visual understanding in pursuit of efficiency. |
| Approach: | They propose a model-agnostic method that integrates visual saliency and text relevance to reconcile efficiency with understanding by integrating visual salions and text relevant. |
| Outcome: | The proposed method outperforms state-of-the-art methods on LLaVA-NeXT . it achieves 13 decrease in FLOPs while maintaining 97% of original performance . |
Correlation-Aware Example Selection for In-Context Learning with Nonsymmetric Determinantal Point Processes (2025.emnlp-main)
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Qiunan Du, Zhiliang Tian, Zhen Huang, Kailun Bian, Tianlun Liu, Zhaoning Zhang, Xinwang Liu, Feng Liu, Dongsheng Li
| Challenge: | Existing studies on in-context learning (ICL) focus on the selection of individual examples and ignore correlations among examples. |
| Approach: | They propose a method to capture positive and negative correlations using the determinantal point process . they optimize the method via kernel decomposition-based MLE to fit a constructed pseudo-labeled dataset . |
| Outcome: | The proposed method outperforms baselines in ICL example selection. |
Principled Content Selection to Generate Diverse and Personalized Multi-Document Summaries (2025.acl-long)
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| Challenge: | Large language models exhibit the _”lost in the middle” phenomenon when they are unevenly attending to different parts of the provided context. |
| Approach: | They propose principled content selection as a way to increase source coverage . they use determinantal point processes to prioritize diverse content . |
| Outcome: | The proposed method improves source coverage on the DiverseSumm benchmark. |
Towards Visually Grounded Multimodal Summarization via Cross-Modal Transformer and Gated Attention (2026.findings-acl)
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| Challenge: | Existing methods for multimodal summarization often inject shallow visual features into deep models, leading to representational mismatches and weak cross-modal grounding. |
| Approach: | They propose a framework that performs text summarization and representative image selection . a deep visual processor aligns the visual encoder with the language model at corresponding depths . |
| Outcome: | The proposed framework produces more accurate, visually grounded summaries and selects more representative images. |