Query-sensitive mutual reinforcement chain and its application in query-oriented multi-document summarization

ABSTRACT

Sentence ranking is the issue of most concern in document summarization. Early researchers have presented the mutual reinforcement principle (MR) between sentence and term for simultaneous key phrase and salient sentence extraction in generic single-document summarization. In this work, we extend the MR to the mutual reinforcement chain (MRC) of three different text granularities, i.e., document, sentence and terms. The aim is to provide a general reinforcement framework and a formal mathematical modeling for the MRC. Going one step further, we incorporate the query influence into the MRC to cope with the need for query-oriented multi-document summarization. While the previous summarization approaches often calculate the similarity regardless of the query, we develop a query-sensitive similarity to measure the affinity between the pair of texts. When evaluated on the DUC 2005 dataset, the experimental results suggest that the proposed query-sensitive MRC (Qs-MRC) is a promising approach for summarization.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.m MISCELLANEOUS

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

General Terms:
Algorithms, Languages

Keywords:
mutual reinforcement chain, query-oriented summarization, query-sensitive similarity, ranking algorithms