  Argument retrieval is the task of finding relevant arguments for a given
query. While existing approaches rely solely on the semantic alignment of
queries and arguments, this first shared task on perspective argument retrieval
incorporates perspectives during retrieval, accounting for latent influences in
argumentation. We present a novel multilingual dataset covering demographic and
socio-cultural (socio) variables, such as age, gender, and political attitude,
representing minority and majority groups in society. We distinguish between
three scenarios to explore how retrieval systems consider explicitly (in both
query and corpus) and implicitly (only in query) formulated perspectives. This
paper provides an overview of this shared task and summarizes the results of
the six submitted systems. We find substantial challenges in incorporating
perspectivism, especially when aiming for personalization based solely on the
text of arguments without explicitly providing socio profiles. Moreover,
retrieval systems tend to be biased towards the majority group but partially
mitigate bias for the female gender. While we bootstrap perspective argument
retrieval, further research is essential to optimize retrieval systems to
facilitate personalization and reduce polarization.
