Diversifying Citation Contexts in Academic Literature for Knowledge Recommendation

Citation contexts of an article refer to sentences or paragraphs that cite that article. Citation contexts are especially useful for recommendation and summarization tasks. However, few studies have recognized the diversity of these citation contexts, thus leading to redundant recommendation lists and abstract [3]. To address this gap, we compared several strategies that can recommend a set of diverse citation contexts by re-ranking extracted citation contexts. Diversification was achieved by combining one of two semantic distance algorithms with one of two re-ranking algorithms. Experimenting with CiteSeerX dataset, our program produced a diverse list of 10 citation contexts that could be recommended to users. We evaluated the experiment results based on a user case study of 15 articles. The case study revealed that a diversity strategy that combined the "ESA" and "MMR" led to a better reading experience for participants compared to other diversity strategies. Our study provides insights to develop better automatic academic recommendation and summarization systems.