@article{GUDER2026101970,
title = {Sentence representations for semantic textual similarity: A systematic review},
journal = {Computer Speech & Language},
volume = {100},
pages = {101970},
year = {2026},
issn = {0885-2308},
doi = {https://doi.org/10.1016/j.csl.2026.101970},
url = {https://www.sciencedirect.com/science/article/pii/S0885230826000331},
author = {Larissa Guder and João Paulo Aires and Hígor Uélinton {da Silva} and Felipe Meneguzzi and Dalvan Griebler},
keywords = {Sentence embeddings, Semantic similarity, Literature review, Natural language processing},
abstract = {In natural language processing (NLP), generating semantically-rich representations of sentences can improve performance on multiple tasks, such as question answering, duplicate detection, sentiment analysis, and machine translation. Recent approaches to NLP using machine learning can produce text representations that carry syntactic and semantic information. This article surveys recent works on generating sentence representations for semantic textual similarity tasks. We conduct our survey using a systematic literature review approach. We retrieve papers from several digital libraries and summarize their key techniques and findings. We propose a taxonomy to facilitate the understanding of the semantic textual similarity task on the sentence level. In our analysis, we describe the current state-of-the-art in sentence representation for semantic textual similarity and propose a guideline for working on this task.}
}