Abstract. The constantly increasing number of scientific publications affects researchers,students, academic hiring officials and search engines alike in discerning the high-impact works among them. Therefore, there is a need to develop methods to rankscientific papers. Despite a prolific literature on query-independent (or static) paperranking algorithms, which aim to rank papers based on their impact, no systematicreview of the field has been conducted. Past literature lacks in terms of definingimpact, often failing to discern among short- term and long-term scientific impact.Further, no extensive experimental evaluation of the various proposed methods hasbeen conducted.This thesis examines impact-based paper ranking in terms of methods, searchengine applications, and its relation to paper abstract readability.