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    <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.</abstract>
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%0 Conference Proceedings
%T Interpretable Text Embeddings and Text Similarity Explanation: A Survey
%A Opitz, Juri
%A Moeller, Lucas
%A Michail, Andrianos
%A Padó, Sebastian
%A Clematide, Simon
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F opitz-etal-2025-interpretable
%X 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.
%R 10.18653/v1/2025.emnlp-main.1135
%U https://aclanthology.org/2025.emnlp-main.1135/
%U https://doi.org/10.18653/v1/2025.emnlp-main.1135
%P 22303-22319

Markdown (Informal)

[Interpretable Text Embeddings and Text Similarity Explanation: A Survey](https://aclanthology.org/2025.emnlp-main.1135/) (Opitz et al., EMNLP 2025)

• Interpretable Text Embeddings and Text Similarity Explanation: A Survey (Opitz et al., EMNLP 2025)

ACL

• Juri Opitz, Lucas Moeller, Andrianos Michail, Sebastian Padó, and Simon Clematide. 2025. Interpretable Text Embeddings and Text Similarity Explanation: A Survey. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 22303–22319, Suzhou, China. Association for Computational Linguistics.