Tiered sentence based topic model for multi-document summarization

Abstract

Abstract In this work, a probabilistic two level topic model named Tiered Sentence based Topic Model is proposed which models the document at sentence and word levels and infer hierarchical latent topics for sentences. The proposed model uses two latent variables for the generation of words- a super topic and a subtopic for each sentence of the document, to model word groupings at sentence level. Popular super topics identify general theme of the documents and are used for selecting summary sentences. The model parameters are used for ranking sentences considering sentence importance and topic coverage. Collapsed Gibbs sampling is used for inference and parameter estimation. The proposed model is used to compare with two sentence based topic models- SenLDA and LDCC on query focused multi-document summarization task, over standard DUC2005 dataset using ROUGE-1 and ROUGE-2 precision and recall scores. The proposed model performs better than Latent Dirichlet Allocation and SenLDA but has been outperformed by LDCC.