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ABSTRACT

This paper deals with our recent research in text summarization. The field has moved from multi-document summarization to update summarization. When producing an update summary of a set of topic-related documents the summarizer assumes prior knowledge of the reader determined by a set of older documents of the same topic. The update summarizer thus must solve a novelty vs. redundancy problem. We describe the development of our summarizer which is based on Iterative Residual Rescaling (IRR) that creates the latent semantic space of a set of documents under consideration. IRR generalizes Singular Value Decomposition (SVD) and enables to control the influence of major and minor topics in the latent space. Our sentence-extractive summarization method computes the redundancy, novelty and significance of each topic. These values are finally used in the sentence selection process. The sentence selection component prevents inner summary redundancy. The results of our participation in TAC evaluation seem to be promising.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
          Subjects: Abstracting methods

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
          Subjects: Linguistic processing

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.7 Natural Language Processing
          Subjects: Text analysis

General Terms:
Algorithms, Experimentation, Languages

Keywords:
iterative residual rescaling, latent semantic analysis, summary evaluation, text summarization