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.