  Opinion pieces (or op-eds) can provide valuable perspectives, but they often
represent only one side of a story, which can make readers susceptible to
confirmation bias and echo chambers. Exposure to different perspectives can
help readers overcome these obstacles and form more robust, nuanced views on
important societal issues. We designed ArguMentor, a human-AI collaboration
system that highlights claims in opinion pieces, identifies counter-arguments
for them using a LLM, and generates a context-based summary of based on current
events. It further enhances user understanding through additional features like
a Q&A bot (that answers user questions pertaining to the text), DebateMe (an
agent that users can argue any side of the piece with) and highlighting (where
users can highlight a word or passage to get its definition or context). Our
evaluation shows that participants can generate more arguments and
counter-arguments and have, on average, have more moderate views after engaging
with the system.
