An Aspect-Driven Random Walk Model for Topic-Focused Multi-document Summarization

  • Yllias Chali
  • Sadid A. Hasan
  • Kaisar Imam
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7097)

Abstract

Recently, there has been increased interest in topic-focused multi-document summarization where the task is to produce automatic summaries in response to a given topic or specific information requested by the user. In this paper, we incorporate a deeper semantic analysis of the source documents to select important concepts by using a predefined list of important aspects that act as a guide for selecting the most relevant sentences into the summaries. We exploit these aspects and build a novel methodology for topic-focused multi-document summarization that operates on a Markov chain tuned to extract the most important sentences by following a random walk paradigm. Our evaluations suggest that the augmentation of important aspects with the random walk model can raise the summary quality over the random walk model up to 19.22%.

Keywords

Topic-focused summarization multi-document summarization aspects random walk model 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yllias Chali
    • 1
  • Sadid A. Hasan
    • 1
  • Kaisar Imam
    • 1
  1. 1.University of LethbridgeLethbridgeCanada

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