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Accelerating Scientific Discovery with Multi-Document Summarization of Impact-Ranked Papers
Paris Koloveas, Serafeim Chatzopoulos, Dionysis Diamantis, Christos Tryfonopoulos, Thanasis Vergoulis
https://arxiv.org/abs/2508.03962

Accelerating Scientific Discovery with Multi-Document Summarization of Impact-Ranked Papers
The growing volume of scientific literature makes it challenging for scientists to move from a list of papers to a synthesized understanding of a topic. Because of the constant influx of new papers on a daily basis, even if a scientist identifies a promising set of papers, they still face the tedious task of individually reading through dozens of titles and abstracts to make sense of occasionally conflicting findings. To address this critical bottleneck in the research workflow, we introduce a …

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https://fediscience.org/@sascha_wolfer/114913920280700941

A Common Pool of Privacy Problems: Legal and Technical Lessons from a Large-Scale Web-Scraped Machine Learning Dataset
Rachel Hong, Jevan Hutson, William Agnew, Imaad Huda, Tadayoshi Kohno, Jamie Morgenstern
https://arxiv.org/abs/2506.17185

A Common Pool of Privacy Problems: Legal and Technical Lessons from a Large-Scale Web-Scraped Machine Learning Dataset
We investigate the contents of web-scraped data for training AI systems, at sizes where human dataset curators and compilers no longer manually annotate every sample. Building off of prior privacy concerns in machine learning models, we ask: What are the legal privacy implications of web-scraped machine learning datasets? In an empirical study of a popular training dataset, we find significant presence of personally identifiable information despite sanitization efforts. Our audit provides concr…