# Explainable Graph Spectral Clustering For GloVe-like Text Embeddings

arXiv: https://arxiv.org/abs/2508.14075 · HTML: https://mlsanity.com/paper/2508.14075/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 32 references that are in the map.
- First posted on arXiv 12 Aug 2025.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Convolutional Neural Network Architectures for Matching Natural Language Sentences](https://mlsanity.com/paper/1503.03244/) (2015)
- [word2vec Parameter Learning Explained](https://mlsanity.com/paper/1411.2738/) (2014)
- [An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation](https://mlsanity.com/paper/1607.05368/) (2016)
- [Representation learning for very short texts using weighted word embedding aggregation](https://mlsanity.com/paper/1607.00570/) (2016)
- [Explainable Graph Spectral Clustering of Text Documents](https://mlsanity.com/paper/2308.00504/) (2023)
- [A Method for Handling Negative Similarities in Explainable Graph Spectral Clustering of Text Documents -- Extended Version](https://mlsanity.com/paper/2504.12360/) (2025)

## Abstract (arXiv)

In a previous paper, we proposed an introduction to the explainability of Graph Spectral Clustering results for textual documents, given that document similarity is computed as cosine similarity in term vector space. In this paper, we generalize this idea by considering other embeddings of documents, in particular, based on the GloVe embedding idea.