Papers with determinantal point processes
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. |
Diversity-Aware Batch Active Learning for Dependency Parsing (2021.naacl-main)
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| Challenge: | a high annotation cost for dependency parsers is a challenge . batch active learning (AL) is based on batch mode, which is more efficient for annotators to label in bulk. |
| Approach: | They propose to reduce the number of labeled examples needed to train a strong dependency parser using batch active learning. |
| Outcome: | The proposed approach improves on an English newswire corpus by enforcing diversity in the sampled batches. |
Diverse and Non-redundant Answer Set Extraction on Community QA based on DPPs (2020.coling-main)
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| Challenge: | Community-based question answering platforms take time to get useful information from among many answers. |
| Approach: | They propose a method to select a diverse and non-redundant answer set rather than ranking the answers. |
| Outcome: | The proposed method outperforms baseline methods on a Japanese CQA site . it calculates the answer importance and similarity between answers by using BERT . |
Better Highlighting: Creating Sub-Sentence Summary Highlights (2020.emnlp-main)
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| Challenge: | Abstractive summarizations are considered to be less reliable because they distort the original meaning and can be confusing for readers. |
| Approach: | They propose a method to generate summary highlights that are understandable on their own to avoid confusion. |
| Outcome: | The proposed method allows summaries to be understood in context and avoids misdirecting readers to false conclusions. |
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. |