Similarity Measure Impact on Transformer-Based Document Clustering Quality

Abstract

Abstract: This report synthesises findings from 12 peer-reviewed papers addressing the following research question: How does the choice of similarity measure (e.g., cosine, Jaccard, Euclidean) impact the cluster quality of transformer-based document embeddings (e.g., BERT, RoBERTa) when evaluated using adjusted. This paper presents SimCSE, a simple contrastive learning framework that greatly advances the state-of-the-art sentence embeddings. We first describe an unsupervised approach, which takes an input sentence and predicts itself in a contrastive objective, with only standard. 15 claims were extracted from source literature; 13 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.7/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the choice of similarity measure (e.g., cosine, Jaccard, Euclidean) impact the cluster quality of transformer-based document embeddings (e.g., BERT, RoBERTa) when evaluated using adjusted Rand index and silhouette score on standard text clustering benchmarks?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance