Most of the existing multi-document summarization methods decompose the documents into sentences and work directly in the sentence space using a term-sentence matrix. However, the knowledge on the document side, i.e. the topics embedded in the documents, can help the context understanding and guide the sentence selection in the summarization procedure. In this paper, we propose a new Bayesian sentence-based topic model for summarization by making use of both the term-document and term-sentence associations. An efficient variational Bayesian algorithm is derived for model parameter estimation. Experimental results on benchmark data sets show the effectiveness of the proposed model for the multi-document summarization task.
REF proposed a new Bayesian sentence-based topic model by making use of both the term-document and termsentence associations to improve the performance of sentence selection.
Multi-Document Summarization using Sentence-based Topic Models
ACL-IJCNLP: Short Papers
Computer Science