Visualizing timelines: evolutionary summarization via iterative reinforcement between text and image streams

  • Authors:

  • Rui Yan;Xiaojun Wan;Mirella Lapata;Wayne Xin Zhao;Pu-Jen Cheng;Xiaoming Li

  • Affiliations:

  • National Taiwan University, Taipei, China;Peking University, Beijing, China;University of Edinburgh, Edinburgh, United Kingdom;Peking University, Beijing, China;National Taiwan University, Taipei, Taiwan Roc;Beihang University, Beijing, China

  • Venue:
  • Proceedings of the 21st ACM international conference on Information and knowledge management
  • Year:
  • 2012

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Abstract

We present a novel graph-based framework for timeline summarization, the task of creating different summaries for different timestamps but for the same topic. Our work extends timeline summarization to a multimodal setting and creates timelines that are both textual and visual. Our approach exploits the fact that news documents are often accompanied by pictures and the two share some common content. Our model optimizes local summary creation and global timeline generation jointly following an iterative approach based on mutual reinforcement and co-ranking. In our algorithm, individual summaries are generated by taking into account the mutual dependencies between sentences and images, and are iteratively refined by considering how they contribute to the global timeline and its coherence. Experiments on real-world datasets show that the timelines produced by our model outperform several competitive baselines both in terms of ROUGE and when assessed by human evaluators.