Exploring Differential Topic Models for Comparative Summarization of Scientific Papers
This paper investigates differential topic models (dTM) for summarizing the differences among document groups. Starting from a simple probabilistic generative model, we propose dTM SAGE that explicitly models the deviations on group specific word distributions to indicate how words are used differen tially across different document groups from a background word distribution. It is more effective to capture unique characteristics for comparing document groups. To generate dTM based comparative summaries, we propose two sentence scoring methods for measuring the sentence discriminative capacity. Experimental results on scientific papers dataset show that our dTM based comparative summari zation methods significantly outperform the generic baselines and the state of the art comparative summarization methods under ROUGE metrics.
COLING