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[2602.22220] What Makes an Ideal Quote? Recommending "Unexpected yet Rational" Quotations via Novelty
Summary
This article presents a novel framework for recommending quotations that are both unexpected and rational, enhancing the writing experience by focusing on deeper semantic properties rather than just topical relevance.
Why It Matters
The ability to recommend quotations that resonate on a deeper level can significantly improve content creation, making it more engaging and meaningful. This research addresses a gap in existing systems that overlook the aesthetic and semantic qualities of quotes, which are crucial for effective communication.
Key Takeaways
- Novelty is identified as a key factor in quotation preference.
- Existing models struggle with understanding the deeper meanings of quotations.
- The NovelQR framework enhances quote recommendation by focusing on contextually novel yet semantically coherent quotes.
- User studies indicate that the proposed system outperforms existing methods in terms of engagement and appropriateness.
- This research has implications for improving AI-driven content generation tools.
Computer Science > Information Retrieval arXiv:2602.22220 (cs) [Submitted on 15 Dec 2025] Title:What Makes an Ideal Quote? Recommending "Unexpected yet Rational" Quotations via Novelty Authors:Bowei Zhang, Jin Xiao, Guanglei Yue, Qianyu He, Yanghua Xiao, Deqing Yang, Jiaqing Liang View a PDF of the paper titled What Makes an Ideal Quote? Recommending "Unexpected yet Rational" Quotations via Novelty, by Bowei Zhang and 5 other authors View PDF HTML (experimental) Abstract:Quotation recommendation aims to enrich writing by suggesting quotes that complement a given context, yet existing systems mostly optimize surface-level topical relevance and ignore the deeper semantic and aesthetic properties that make quotations memorable. We start from two empirical observations. First, a systematic user study shows that people consistently prefer quotations that are ``unexpected yet rational'' in context, identifying novelty as a key desideratum. Second, we find that strong existing models struggle to fully understand the deep meanings of quotations. Inspired by defamiliarization theory, we therefore formalize quote recommendation as choosing contextually novel but semantically coherent quotations. We operationalize this objective with NovelQR, a novelty-driven quotation recommendation framework. A generative label agent first interprets each quotation and its surrounding context into multi-dimensional deep-meaning labels, enabling label-enhanced retrieval. A token-level novelty estima...