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Comparative Personalization for Multi-document Summarization

arXiv 2509.21562 ↗First posted 25 Sep 2025cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 33 references that are in the map. First posted on arXiv 25 Sep 2025.

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References · 18

  1. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  2. 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,276
  3. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,282
  4. 2023Towards General Text Embeddings with Multi-stage Contrastive Learningcited by 723
  5. 2020SummEval: Re-evaluating Summarization Evaluationcited by 674
  6. 2023LaMP: When Large Language Models Meet Personalizationcited by 437
  7. 2019Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Modelcited by 431
  8. 2023Personalized Soups: Personalized Large Language Model Alignment via Post-hoc Parameter Mergingcited by 280
  9. 2024Can LLM be a Personalized Judge?cited by 100
  10. 2019Unsupervised Opinion Summarization as Copycat-Review Generationcited by 68
  11. 2022POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detectioncited by 51
  12. 2024Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data Refinementcited by 49
6 more
  1. 2023Teach LLMs to Personalize -- An Approach inspired by Writing Educationcited by 48
  2. 2023Embrace Divergence for Richer Insights: A Multi-document Summarization Benchmark and a Case Study on Summarizing Diverse Information from News Articlescited by 45
  3. 2025Measuring What Makes You Unique: Difference-Aware User Modeling for Enhancing LLM Personalizationcited by 45
  4. 2024Can Large Language Models Identify Authorship?cited by 41
  5. 2025ExPerT: Effective and Explainable Evaluation of Personalized Long-Form Text Generationcited by 26
  6. 2024Customizing Language Model Responses with Contrastive In-Context Learningcited by 19

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