Abstract:Opinion pieces often represent only one side of any story, which can influence users and make them susceptible to confirmation bias and echo chambers in society. Moreover, humans are also bad at reading long articles -- often indulging in idle reading and re-reading. To solve this, we design ArguMentor, an end-to-end system that highlights claims in opinion pieces, generates counter-arguments for them using an LLM, and generates a context-based summary of the passage based on current events. It further enhances user interaction and understanding through additional features like Q&A bot, DebateMe and highlighting trigger windows. Our survey and results show that users can generate more counterarguments and on an average have more neutralized views after engaging with the system.