Integrating clustering and multi-document summarization to improve document understanding

ABSTRACT

Document understanding techniques such as document clustering and multi-document summarization have been receiving much attention in recent years. Current document clustering methods usually represent documents as a term-document matrix and perform clustering algorithms on it. Although these clustering methods can group the documents satisfactorily, it is still hard for people to capture the meanings of the documents since there is no satisfactory interpretation for each document cluster. In this paper, we propose a new language model to simultaneously cluster and summarize the documents. By utilizing the mutual influence of the document clustering and summarization, our method makes (1) a better document clustering method with more meaningful interpretation and (2) a better document summarization method taking the document context information into consideration.

REFERENCES

• 1: H. Cho, I. Dhillon, Y. Guan, and S. Sra. Minimum sum squared residue co-clustering. In Proceedings of SIAM Data Mining 2004.
• 2: Inderjit S. Dhillon , Subramanyam Mallela , Dharmendra S. Modha, Information-theoretic co-clustering, Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, August 24-27, 2003, Washington, D.C. [doi>10.1145/956750.956764]
• 3: Chris Ding , Tao Li , Wei Peng , Haesun Park, Orthogonal nonnegative matrix t-factorizations for clustering, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, August 20-23, 2006, Philadelphia, PA, USA [doi>10.1145/1150402.1150420]
• 4: Thomas Hofmann, Probabilistic latent semantic indexing, Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, p.50-57, August 15-19, 1999, Berkeley, California, United States [doi>10.1145/312624.312649]
• 5: D. D. Lee and H. S. Seung. Algorithms for non-negative matrix factorization. In Proceedings of NIPS 2001.
• 6: Wei Xu , Xin Liu , Yihong Gong, Document clustering based on non-negative matrix factorization, Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, July 28-August 01, 2003, Toronto, Canada [doi>10.1145/860435.860485]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning

General Terms:
Algorithms, Experimentation, Performance

Keywords:
document clustering, multi-document summarization, nonnegative matrix factorization with given bases