Version 2.0 - EmbedSLR: An open-source python framework for efficient embedding-based screening and bibliometric validation in systematic literature review
Abstract
EmbedSLR 2.0 features a multi-embedding agent system that simulates teamwork - a vital aspect of systematic literature reviews (SLR) and evaluates it using bibliometric metrics. Experiments demonstrate that the models' effectiveness varies significantly and that publications selected by consensus outperform those chosen by individual models. EmbedSLR 2.0 allows users to choose the best model for a specific research topic or further enhance the matching of the selected publication to the research subject through model consensus. Building on v1.0, this version further improves effectiveness, introduces results visualization, supports both local and Colab environments, ensures reproducibility, and remains easy to use.
Fields of Study & Subjects
Meta-analysis and systematic reviews
Scientific Computing and Data Management
Biomedical Text Mining and Ontologies
Publication details
- Journal / source
- SoftwareX
- Publisher
- Elsevier BV
- Volume
- 34
- Pages
- 102563–102563
- Publication date
- 19 February 2026
- Language
- EN