Research map / Learning theory and algorithms / Clustering
Summaries as Centroids for Interpretable and Scalable Text Clustering
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Fair and Fast k-Center Clustering for Data Summarization (ICML 2022 · similarity 0.88)
- X-DMM: Fast and Scalable Model Based Text Clustering (AAAI 2019 · similarity 0.85)
- KwikBucks: Correlation Clustering with Cheap-Weak and Expensive-Strong Signals (ICLR 2023 · similarity 0.84)
- Optimized Algorithms for Text Clustering with LLM-Generated Constraints (AAAI 2026 · similarity 0.84)
- BooookScore: A systematic exploration of book-length summarization in the era of LLMs (ICLR 2024 · similarity 0.84)
- SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization (ICML 2025 · similarity 0.83)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.