Papers with density-based clustering
Media-to-Insights: A Multi-Agent AI System for Continuous Media Monitoring, Analysis, and Reporting (2026.acl-demo)
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Ashraf Hatim Elneima, Ozan Yilmaz, Hadi Nasrallah, Sanjika Hewavitharana, Mohamed Al-Badrashiny, Hassan Sawaf
| Challenge: | Existing systems that use keyword-based media monitoring miss semantically relevant articles and are expensive at scale. |
| Approach: | They propose a multi-agent media monitoring system that processes streaming articles through three stages: article matching, batched feature extraction, and report generation with deterministic deduplication and density-based clustering. |
| Outcome: | The proposed system reduces agent invocations by 20% and reduces core feature extraction calls from 7 to 2 per article - a 71% reduction - with bounded quality tradeoffs . |
Extracting Age-Related Stereotypes from Social Media Texts (2022.lrec-1)
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| Challenge: | a method for extracting age-related stereotypes from Twitter data is under-studied in NLP . stereotyping on the basis of protected characteristics has been understudied . |
| Approach: | They propose a method for extracting age-related stereotypes from Twitter data . they generate a corpus of 300,000 over-generalizations about four contemporary generations . |
| Outcome: | The method uncovers common stereotypes as reported in media and psychological literature . it also finds that stereotypes for different generations vary across topics . |
ClusterRAG: Cluster-Based Collaborative Filtering for Personalized Retrieval-Augmented Generation (2026.acl-long)
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| Challenge: | Existing approaches to Personalized Retrieval-Augmented Generation (RAG) ignore long-term user information and inter-user relationships when constructing retrieval contexts, limiting personalization and the ability to leverage analogous users' knowledge for improved generation quality. |
| Approach: | They propose a Cluster-Based Collaborative Filtering for Personalized Retrieval-Augmented Generation that organizes users into semantically coherent clusters and performs retrieval at both the cluster and document levels via cluster-level similarity and fine-grained ranking. |
| Outcome: | Extensive experiments on the LaMP benchmark show that ClusterRAG integrates seamlessly with different dense retrievers and rankers, and remains effective when paired with both fine-tuned and zero-shot language models. |