Singh, ShrutiShrutiSinghSingh, MayankMayankSinghGoyal, PawanPawanGoyal2025-08-312025-08-312021-01-01[9781665417709]10.1109/JCDL52503.2021.000682-s2.0-85119868210http://repository.iitgn.ac.in/handle/IITG2025/25608Comparing research papers is a conventional method to demonstrate progress in experimental research. We present COMPARE, a taxonomy and a dataset of comparison discussions in peer reviews of research papers in the domain of experimental deep learning. From a thorough observation of a large set of review sentences, we build a taxonomy of categories in comparison discussions and present a detailed annotation scheme to analyze this. Overall, we annotate 117 reviews covering 1, 800 sentences. We experiment with various methods to identify comparison sentences in peer reviews and report a maximum F1 Score of 0.49. We also pretrain two language models specifically on ML, NLP, and CV paper abstracts and reviews to learn informative representations of peer reviews.falsePeer Review | Scientometrics | TaxonomyConference Paper238-241202173