通过用户间对比实现更精准的多文档摘要个性化
Comparative Personalization for Multi-document Summarization
- 基于用户偏好对比生成结构化分析，指导摘要生成
- 在新构建的数据集上超越现有基线模型
- 适合需要高个性化摘要的应用场景
个性化多文档摘要对满足用户写作风格和内容关注点差异至关重要。本文提出，有效个性化需通过比较目标用户与其他用户偏好来识别细微差异。为此，我们提出ComPSum框架：首先生成用户偏好对比的结构化分析，再以此引导摘要生成。为评估性能，我们设计AuthorMap——一种细粒度无参考评价框架，通过判断不同用户生成摘要的作者归属一致性来衡量个性化程度。为支持稳健评估，我们构建了覆盖评论与新闻领域的个性化多文档摘要数据集PerMSum。在PerMSum上使用AuthorMap评估ComPSum，结果表明其优于多个强基线模型。
原文摘要 · Abstract (English)
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.
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