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.

ArguMentor 设计了一个全套系统，它突出了意见文章中的论点，并利用 LLM 为其生成反驳观点，然后根据当前事件为文章生成基于上下文的摘要。通过额外的功能如问答机器人，辩论和突出触发窗口，该系统进一步增强了用户的交互和理解。我们的调查和结果显示，用户在使用该系统后能够产生更多的反驳观点，并且平均具有更多中立的观点。

ArguMentor: 提升用户体验与对立观点

ArguMentor: Augmenting User Experiences with Counter-Perspectives

The goal of the Fast Abstracts track is to bring together researchers and
practitioners working on dependable computing to discuss work in progress or
opinion pieces. Contributions are welcome from academia and industry. Fast
Abstracts aim to serve as a rapid and flexible mechanism to: (i) Report on
current work that may or may not be complete; (ii) Introduce new ideas to the
community; (iii) State positions on controversial issues or open problems; (iv)
Share lessons learnt from real-word dependability engineering; and (v) Debunk
or question results from other papers based on contra-indications. The Student
Forum aims at creating a vibrant and friendly environment where students can
present and discuss their work, and exchange ideas and experiences with other
students, researchers and industry. One of the key goals of the Forum is to
provide students with feedback on their preliminary results that might help
with their future research directions.

该研究论文介绍了关于可靠计算的快速摘要，包括学术界和工业界的贡献以及学生论坛的目标，旨在提供学生初步研究结果的反馈，帮助他们的未来研究方向。