Echoes of Citations: Automated Extraction of Claims from Full Scientific Papers

Abstract

Automated extraction of core scientific claims, concise statements of a paper’s primary contributions, is critical for navigating the growing scientific literature. We present a scalable framework that leverages citances, sentences from other papers citing the target work, as natural supervision, removing the need for costly manual labelling. Our method filters citances with a claim-focused rubric and aligns them with candidate claims to train two pipelines: an unsupervised extractor and a weakly supervised model. Experiments show our approach outperforms existing baselines, achieving up to 18% higher precision and 22% greater coverage. We further analyse claim distributions across paper sections and introduce a taxonomy of claim types, providing new insights into the rhetorical structure of scientific discourse.