What Makes an Ideal Quote? Recommending "Unexpected yet Rational" Quotations via Novelty

TL;DR

This paper introduces NovelQR, a framework for recommending quotations that are both unexpected and rational, emphasizing deep semantic understanding and novelty to enhance writing and engagement.

Contribution

The paper formalizes quote recommendation as balancing novelty and semantic coherence, and develops NovelQR with deep-meaning labels and a novelty estimator, outperforming existing methods.

Findings

Human judges prefer NovelQR's quotations for appropriateness, novelty, and engagement.

NovelQR matches or surpasses existing methods in novelty estimation.

Experiments on bilingual datasets demonstrate the effectiveness of NovelQR.

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 estimator then reranks candidates while mitigating auto-regressive continuation bias. Experiments on bilingual datasets spanning diverse real-world domains show that our system recommends quotations that human judges rate as more appropriate, more novel, and more engaging than other baselines, while matching or surpassing existing methods in novelty estimation.