@inproceedings{opitz-etal-2025-interpretable,
    title = "Interpretable Text Embeddings and Text Similarity Explanation: A Survey",
    author = "Opitz, Juri  and
      Moeller, Lucas  and
      Michail, Andrianos  and
      Pad{\'o}, Sebastian  and
      Clematide, Simon",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1135/",
    pages = "22314--22330",
    ISBN = "979-8-89176-332-6",
    abstract = "Text embeddings are a fundamental component in many NLP tasks, including classification, regression, clustering, and semantic search. However, despite their ubiquitous application, challenges persist in interpreting embeddings and explaining similarities between them.In this work, we provide a structured overview of methods specializing in inherently interpretable text embeddings and text similarity explanation, an underexplored research area. We characterize the main ideas, approaches, and trade-offs. We compare means of evaluation, discuss overarching lessons learned and finally identify opportunities and open challenges for future research."
}