Navigating the Topical Structure of Academic Search Results via the Wikipedia Category Network

Searching for scientific publications on the Web is a tedious task, especially when exploring an unfamiliar domain. Typical scholarly search engines produce lengthy unstructured result lists that are difficult to comprehend, interpret and browse. We propose a novel method of organizing the search results into concise and informative topic hierarchies. The method consists of two steps: extracting interrelated topics from the result set, and summarizing the topic graph. In the first step we map the search results to articles and categories of Wikipedia, constructing a graph of relevant topics with hierarchical relations. In the second step we sequentially build nested summaries of the produced topic graph using a structured output prediction approach. Trained on a small number of examples, our method learns to construct informative summaries for unseen topic graphs, and outperforms unsupervised state-of-the-art Wikipedia-based clustering. Copyright is held by the owner/author(s).