UserSumBench: A Benchmark Framework for Evaluating User Summarization Approaches

• URL: http://arxiv.org/abs/2408.16966v2
• Date: Thu, 5 Sep 2024 23:18:00 GMT
• Title: UserSumBench: A Benchmark Framework for Evaluating User Summarization Approaches
• Authors: Chao Wang, Neo Wu, Lin Ning, Jiaxing Wu, Luyang Liu, Jun Xie, Shawn O'Banion, Bradley Green,
• Abstract summary: Large language models (LLMs) have shown remarkable capabilities in generating user summaries from a long list of raw user activity data. These summaries capture essential user information such as preferences and interests, and are invaluable for personalization applications. However, the development of new summarization techniques is hindered by the lack of ground-truth labels, the inherent subjectivity of user summaries, and human evaluation.
• Score: 25.133460380551327
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have shown remarkable capabilities in generating user summaries from a long list of raw user activity data. These summaries capture essential user information such as preferences and interests, and therefore are invaluable for LLM-based personalization applications, such as explainable recommender systems. However, the development of new summarization techniques is hindered by the lack of ground-truth labels, the inherent subjectivity of user summaries, and human evaluation which is often costly and time-consuming. To address these challenges, we introduce \UserSumBench, a benchmark framework designed to facilitate iterative development of LLM-based summarization approaches. This framework offers two key components: (1) A reference-free summary quality metric. We show that this metric is effective and aligned with human preferences across three diverse datasets (MovieLens, Yelp and Amazon Review). (2) A novel robust summarization method that leverages time-hierarchical summarizer and self-critique verifier to produce high-quality summaries while eliminating hallucination. This method serves as a strong baseline for further innovation in summarization techniques.

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