The reliable and early detection of promising research directions is of great practical importance, especially in cases of limited resources. It enables researchers, funding experts, and science authorities to focus their efforts effectively. Although citation analysis has been commonly considered the primary tool to detect directions for a long time, it lacks responsiveness, as it requires time for citations to emerge. In this paper, we propose a conceptual framework that detects new research directions with a contextual Top2Vec model, collects and analyzes reviews for those directions via Transformer-based classifiers, ranks them, and generates short summaries for the highest-scoring ones with a BART model. Averaging review scores for a whole topic helps mitigate the review bias problem. Experiments on past ICLR open reviews show that the highly ranked directions detected are significantly better cited; additionally, in most cases, they exhibit better publication dynamics.
DOI: 10.3390/bdcc9120319
Download the article (PDF) from the MDPI publisher's website: https://www.mdpi.com/2504-2289/9/12/319/pdf
In the Scilit database: https://www.scilit.com/publications/ef304dbbb9e1327fc2ca72eff0732562
Dmitry Devyatkin, Ilya V. Sochenkov, Dmitrii Popov, Denis Zubarev, Anastasia Ryzhova, Fyodor Abanin and Oleg Grigoriev. Identifying New Promising Research Directions with Open Peer Reviews and Contextual Top2Vec // Big Data and Cognitive Computing. 2025, 9(12), 319.