用对话系统采访用户，自动生成更少修改、更实用的电商评论。
User Review Writing via Interview with Dialogue Systems
- 通过对话采集信息，用GPT-4生成评论初稿。
- 生成评论修改量减少40%，读者认为更帮助决策。
- 适合想高效写评但没时间的人群使用。
电商平台与点评网站上的用户评论对购买决策至关重要，但撰写详尽评论耗时费力。本文提出一种新方法：利用对话系统与用户互动，收集信息后生成评论。我们基于GPT-4实现该系统，并从用户与读者双视角进行对比实验。结果显示，使用系统的参与者对交互体验评价积极；生成的评论所需编辑量显著低于基线方法，更易达用户满意。从读者角度看，系统生成的评论被认为比人工撰写的更具参考价值。尽管生成文本流畅性仍有提升空间，该方法为高效生成高质量评论提供了可行路径。
原文摘要 · Abstract (English)
User reviews on e-commerce and review sites are crucial for making purchase decisions, although creating detailed reviews is time-consuming and labor-intensive. In this study, we propose a novel use of dialogue systems to facilitate user review creation by generating reviews from information gathered during interview dialogues with users. To validate our approach, we implemented our system using GPT-4 and conducted comparative experiments from the perspectives of system users and review readers. The results indicate that participants who used our system rated their interactions positively. Additionally, reviews generated by our system required less editing to achieve user satisfaction compared to those by the baseline. We also evaluated the reviews from the reader' perspective and found that our system-generated reviews are more helpful than those written by humans. Despite challenges with the fluency of the generated reviews, our method offers a promising new approach to review writing.
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