Multidocument summarization: An added value to clustering in interactive retrieval

ABSTRACT

A more and more generalized problem in effective information access is the presence in the same corpus of multiple documents that contain similar information. Generally, users may be interested in locating, for a topic addressed by a group of similar documents, one or several particular aspects. This kind of task, called instance or aspectual retrieval, has been explored in several TREC Interactive Tracks. In this article, we propose in addition to the classification capacity of clustering techniques, the possibility of offering a indicative extract about the contents of several sources by means of multidocument summarization techniques. Two kinds of summaries are provided. The first one covers the similarities of each cluster of documents retrieved. The second one shows the particularities of each document with respect to the common topic in the cluster. The document multitopic structure has been used in order to determine similarities and differences of topics in the cluster of documents. The system is independent of document domain and genre. An evaluation of the proposed system with users proves significant improvements in effectiveness. The results of previous experiments that have compared clustering algorithms are also reported.

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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.3.3 Information Search and Retrieval
          Subjects: Clustering; Search process
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.2 User Interfaces (D.2.2, H.1.2, I.3.6)
          Subjects: Evaluation/methodology

General Terms:
Experimentation, Human Factors, Measurement, Performance

Keywords:
Multidocument summarization, topic segmentation