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Summarizing topical contents from PubMed documents using a thematic analysis
BibTeX
@MISC{Kim_summarizingtopical,
author = {Sun Kim and Lana Yeganova and W. John Wilbur},
title = {Summarizing topical contents from PubMed documents using a thematic analysis},
year = {}
}
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Abstract
Improving the search and browsing ex-perience in PubMedr is a key compo-nent in helping users detect information of interest. In particular, when explor-ing a novel field, it is important to pro-vide 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 Adja-cent Violators algorithm to induce signifi-cant themes. Then, for each theme, a title is computed using PubMed document ti-tles and theme-dependent term scores. We tested our system on five disease sets from OMIMr and evaluated the results based on normalized point-wise mutual informa-tion and MeSHr terms. For both perfor-mance measures, the proposed approach outperformed LDA. The quality of theme titles were also evaluated by comparing them with manually created titles. 1
Keyphrases
topical content thematic analysis pubmed document user detect information perfor-mance measure pubmed document ti-tles normalized point-wise mutual informa-tion meshr term signifi-cant theme comprehensive view specific subject thematic clustering algorithm theme title pool adja-cent violator theme-dependent term score key compo-nent disease set novel field panoramic picture
