Summarizing Topical Contents from PubMed Documents Using a Thematic Analysis

Sun Kim, Lana Yeganova, W. John Wilbur

Abstract: Improving the search and browsing experience in PubMed r is a key component in helping users detect information of interest. In particular, when exploring a novel field, it is important to provide a comprehensive view for a specific subject. One solution for providing this panoramic picture is to find sub-topics from a set of documents. We propose a method that finds sub-topics that we refer to as themes and computes representative titles based on a set of documents in each theme. The method combines a thematic clustering algorithm and the Pool Adjacent Violators algorithm to induce significant themes. Then, for each theme, a title is computed using PubMed document titles and theme-dependent term scores. We tested our system on five disease sets from OMIM r and evaluated the results based on normalized point-wise mutual information and MeSH r terms. For both performance measures, the proposed approach outperformed LDA. The quality of theme titles were also evaluated by comparing them with manually created titles.