Searching for a related article based on a reference article is an integral part of scientific research. PubMed, like many academic search engines, has a "similar articles" feature that recommends articles relevant to the current article viewed by a user. Explaining recommended items can be of great utility to users, particularly in the literature search process. With more than a million biomedical papers being published each year, explaining the recommended similar articles would facilitate researchers and clinicians in searching for related articles. Nonetheless, the majority of current literature recommendation systems lack explanations for their suggestions. We employ a post hoc approach to explaining recommendations by identifying relevant tokens in the titles of similar articles. Our major contribution is building PubCLogs by repurposing 5.6 million pairs of coclicked articles from PubMed's user query logs. Using our PubCLogs dataset, we train the Highlight Similar Article Title (HSAT), a transformer-based model designed to select the most relevant parts of the title of a similar article, based on the title and abstract of a seed article. HSAT demonstrates strong performance in our empirical evaluations, achieving an F1 score of 91.72 percent on the PubCLogs test set, considerably outperforming several baselines including BM25 (70.62), MPNet (67.11), MedCPT (62.22), GPT-3.5 (46.00), and GPT-4 (64.89). Additional evaluations on a separate, manually annotated test set further verifies HSAT's performance. Moreover, participants of our user study indicate a preference for HSAT, due to its superior balance between conciseness and comprehensiveness. Our study suggests that repurposing user query logs of academic search engines can be a promising way to train state-of-the-art models for explaining literature recommendation.

翻译：基于一篇参考文献查找相关文章是科学研究的重要组成部分。与许多学术搜索引擎类似，PubMed设有"相似文献"功能，可为用户当前浏览的文章推荐相关文献。对推荐项目进行解释对于用户尤其是文献检索过程具有重要价值。鉴于每年有超过百万篇生物医学论文发表，对推荐相似文献进行解释将有助于研究人员和临床医生检索相关文章。然而，当前多数文献推荐系统缺乏对其推荐建议的解释。我们采用事后解释方法，通过识别相似文献标题中的相关词元来提供推荐解释。主要贡献是构建了PubCLogs数据集，该数据集通过重新利用PubMed用户查询日志中的560万对共引文章而成。利用PubCLogs数据集，我们训练了基于Transformer的"高亮相似文章标题"(HSAT)模型，该模型能够根据种子文章的标题和摘要，选择相似文章标题中最相关的部分。实证评估表明HSAT表现优异，在PubCLogs测试集上F1分数达到91.72%，显著优于包括BM25(70.62)、MPNet(67.11)、MedCPT(62.22)、GPT-3.5(46.00)和GPT-4(64.89)在内的多个基线模型。在独立的人工标注测试集上的额外评估进一步验证了HSAT的性能。此外，用户研究参与者表示更偏好HSAT，因其在简洁性与全面性之间取得了更优平衡。本研究表明，重新利用学术搜索引擎的用户查询日志是训练最新模型以解释文献推荐的一条有前景的途径。