多文档摘要的对比个性化
Comparative Personalization for Multi-document Summarization
AI总结：
本文提出ComPSum框架，通过对比用户偏好生成结构化分析以指导个性化多文档摘要，并构建PerMSum数据集和AuthorMap评估框架，实验证明其优于强基线。
摘要：
个性化多文档摘要（MDS）对于满足用户在写作风格和内容重点方面的个人偏好至关重要。在本文中，我们提出，为了实现有效的个性化，通过将给定用户的偏好与其他用户的偏好进行比较来识别用户偏好之间的细粒度差异非常重要。基于此，我们提出了ComPSum，一个个性化MDS框架。它首先通过比较用户的偏好与其他用户的偏好来生成用户的结构化分析。然后，生成的结构化分析被用来指导个性化摘要的生成。为了评估ComPSum的性能，我们提出了AuthorMap，一个用于个性化MDS的细粒度无参考评估框架。它基于为不同用户生成的两个个性化摘要之间的作者归属来评估系统的个性化程度。为了对个性化MDS进行稳健的评估，我们构建了PerMSum，一个在评论和新闻领域的个性化MDS数据集。我们使用AuthorMap在PerMSum上评估了ComPSum的性能，表明它优于强基线。
英文摘要：
Personalized multi-document summarization (MDS) is essential for meeting individual user preferences of writing style and content focus for summaries. In this paper, we propose that for effective personalization, it is important to identify fine-grained differences between users' preferences by comparing the given user's preferences with other users' preferences.Motivated by this, we propose ComPSum, a personalized MDS framework. It first generates a structured analysis of a user by comparing their preferences with other users' preferences. The generated structured analysis is then used to guide the generation of personalized summaries. To evaluate the performance of ComPSum, we propose AuthorMap, a fine-grained reference-free evaluation framework for personalized MDS. It evaluates the personalization of a system based on the authorship attribution between two personalized summaries generated for different users. For robust evaluation of personalized MDS, we construct PerMSum, a personalized MDS dataset in the review and news domain. We evaluate the performance of ComPSum on PerMSum using AuthorMap, showing that it outperforms strong baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
