Sparse two level topic model for extraction of general summary words

Abstract

Extractive multi-document summarization methods based on topic models find relevant general concepts or topics that are most representative of the documents. These topics are used for sentence ranking and selection. In this paper, a two level topic model using spike and slab prior is proposed that identify better general topics for summarization. Spike and slab prior is used earlier for finding aspect specific topics. Proposed two level model uses spike and slab prior to achieve better general topics at high level of topic hierarchy. Experiments conducted on DUC2007 dataset show that proposed model is able to identify more summary oriented general words and improve ROUGE score.

Subject Classification:

• Primary 93A30
• Secondary 49K15

Keywords:

• Multi-document Summarization
• Sparsity
• Topic Model
• Rouge Score