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Automatic classification of scientific articles based on commoncharacteristics is an interesting problem with many applications in digitallibrary and information retrieval systems. Properly organized articles can beuseful for automatic generation of taxonomies in scientific writings, textualsummarization, efficient information retrieval etc. Generating article bundlesfrom a large number of input articles, based on the associated features of thearticles is tedious and computationally expensive task. In this report wepropose an automatic two-step approach for topic extraction and bundling ofrelated articles from a set of scientific articles in real-time. For topicextraction, we make use of Latent Dirichlet Allocation (LDA) topic modelingtechniques and for bundling, we make use of hierarchical agglomerativeclustering techniques.  We run experiments to validate our bundling semantics and compare it withexisting models in use. We make use of an online crowdsourcing marketplaceprovided by Amazon called Amazon Mechanical Turk to carry out experiments. Weexplain our experimental setup and empirical results in detail and show thatour method is advantageous over existing ones.

Topic Extraction and Bundling of Related Scientific Articles