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arXiv · 2408.16966

UserSumBench: A Benchmark Framework for Evaluating User Summarization Approaches

Chao Wang·Neo Wu·Lin Ning·Jiaxing Wu·Luyang Liu·Jun Xie·Shawn O'Banion·Bradley Green
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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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Chao Wang, Neo Wu, Lin Ning, Jiaxing Wu, Luyang Liu, Jun Xie, Shawn O'Banion, Bradley Green. 2024-08-30. UserSumBench: A Benchmark Framework for Evaluating User Summarization Approaches. https://arxiv.org/abs/2408.16966

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