K-means clustering groups document chunks by semantic similarity so summarization models can work with coherent sections instead of only raw text. It is useful for long or complex documents where simple extraction and compression may not preserve enough context. section 7.1.6 section 7.0
A document-clustering pipeline proceeds through these implementation stages:
- Split the source text into overlapping chunks with RecursiveCharacterTextSplitter. section 7.1.6
- Compute an embedding vector for each chunk with a language model. section 7.1.6
- Apply k-means to group chunks according to similarity in vector space. section 7.1.6
- Compare embedding distances and merge chunks that pass a chosen similarity threshold. section 7.1.6
JavaScript document clustering
A runnable implementation uses ml-kmeans and @huggingface/transformers: it creates a feature-extraction pipeline, embeds every document chunk, rejects an empty input, and derives a cluster count from the number of chunks before clustering.
