Enhancing Event-centric News Cluster Summarization via Data Sharpening and Localization Insights (2025.acl-long)
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| Challenge: | Existing work on text summarization approaches are approaching or exceeding human excellence . |
| Approach: | They propose a framework that optimizes the balance between information volume and entropy in input texts. |
| Outcome: | The proposed framework optimizes information volume and entropy in input texts, achieving notable improvements in localized contexts. |
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| Challenge: | Using hierarchical Dirichlet processes, we characterize news articles associated with key events from news streams. |
| Approach: | They propose a generic framework for news stream clustering that analyzes the temporal trend of news articles to automatically extract the underlying key news events that draw significant media attention. |
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Event-Keyed Summarization (2024.findings-emnlp)
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| Challenge: | a novel task combines document-level event extraction with event-keyed summarization . a recent study has shown that traditional summarizing produces inferior summaries of target events . |
| Approach: | They propose a task that marries traditional summarization and document-level event extraction with the goal of generating a contextualized summary for a specific event, given a document and an extracted event structure. |
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From Moments to Milestones: Incremental Timeline Summarization Leveraging Large Language Models (2024.acl-long)
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| Challenge: | Prior work on timeline summarization has neglected the potential synergy between the two forms of timelines. |
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Multilingual Clustering of Streaming News (D18-1)
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| Challenge: | a novel method for clustering news across languages is proposed . a key challenge in handling news streams is that they must be generated on the fly . |
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GlobeSumm: A Challenging Benchmark Towards Unifying Multi-lingual, Cross-lingual and Multi-document News Summarization (2024.emnlp-main)
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Yangfan Ye, Xiachong Feng, Xiaocheng Feng, Weitao Ma, Libo Qin, Dongliang Xu, Qing Yang, Hongtao Liu, Bing Qin
| Challenge: | Current studies focus on single-language or single-document tasks for news summarization . lack of a benchmark inhibits researchers from adequately studying this invaluable problem. |
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Embrace Divergence for Richer Insights: A Multi-document Summarization Benchmark and a Case Study on Summarizing Diverse Information from News Articles (2024.naacl-long)
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Kung-Hsiang Huang, Philippe Laban, Alexander Fabbri, Prafulla Kumar Choubey, Shafiq Joty, Caiming Xiong, Chien-Sheng Wu
| Challenge: | Existing studies on multi-document summarization focus on collating information that all sources agree upon, but the task of summarizing diverse information remains underexplored. |
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End-to-End Segmentation-based News Summarization (2022.findings-acl)
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| Challenge: | Existing summarization systems only provide one genetic summary of the whole article, making it difficult for users to navigate the reading. |
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A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portal (2020.acl-main)
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| Challenge: | Multidocument summarization (MDS) aims to compress large document collections into short summaries. |
| Approach: | They propose a large-scale multidocument summarization dataset that is large both in total number of document clusters and in the size of individual clusters. |
| Outcome: | The proposed dataset is large both in the total number of document clusters and in the size of individual clusters. |
Synergetic Event Understanding: A Collaborative Approach to Cross-Document Event Coreference Resolution with Large Language Models (2024.acl-long)
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| Challenge: | Existing approaches to cross-document event coreference resolution are prone to learning simple co-occurrences due to the complexity of contexts. |
| Approach: | They propose a collaborative approach to cross-document event coreference resolution that leverages both a universally capable LLM and a task-specific SLM. |
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Multi-News: A Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model (P19-1)
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| Challenge: | Multi-document summarization (MDS) of news articles has been limited to datasets of a couple of hundred examples. |
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