Integrating Clustering and Multi-Document Summarization by Bi-Mixture Probabilistic Latent Semantic Analysis (PLSA) with Sentence Bases

Authors

  • Chao Shen Florida International University
  • Tao Li Florida International University
  • Chris Ding University of Texas at Arlington

Abstract

Probabilistic Latent Semantic Analysis (PLSA) has been popularly used in document analysis. However, as it is currently formulated, PLSA strictly requires the number of word latent classes to be equal to the number of document latent classes. In this paper, we propose Bi-mixture PLSA, a new formulation of PLSA that allows the number of latent word classes to be different from the number of latent document classes. We further extend Bi-mixture PLSA to incorporate the sentence information, and propose Bi-mixture PLSA with sentence bases (Bi-PLSAS) to simultaneously cluster and summarize the documents utilizing the mutual influence of the document clustering and summarization procedures. Experiments on real-world datasets demonstrate the effectiveness of our proposed methods.

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Published

2011-08-04

How to Cite

Shen, C., Li, T., & Ding, C. (2011). Integrating Clustering and Multi-Document Summarization by Bi-Mixture Probabilistic Latent Semantic Analysis (PLSA) with Sentence Bases. Proceedings of the AAAI Conference on Artificial Intelligence, 25(1), 914-920. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/7977

Issue

Section

AAAI Technical Track: Natural Language Processing