Generating Multiple Diverse Summaries

  • Natwar Modani
  • Balaji Vasan Srinivasan
  • Harsh Jhamtani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10041)


Authors often re-purpose existing content to create shorter versions for other channels. Automatic summarization techniques can be used to generate a candidate content that can be further fine-tuned by the author. Existing work in automatic summarization primarily focus on providing a single succinct summary. However, this may not suit the needs of a content author or curator, who may want to repurpose/select the content from several alternative candidates. In this paper, we propose an approach to generate multiple diverse summaries, so that authors can choose an appropriate summary without compromising on the summary quality. Our approach can be utilized in conjunction with a large class of extractive summarization techniques, and we illustrate our approach with several summarization techniques. We experimentally show that our approach results in fairly diverse summaries, without compromising the quality of the summaries with respect to the single summary generated by the corresponding base methods.


Input Text Text Unit Graph Base Approach Single Summary Content Author 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Natwar Modani
    • 1
  • Balaji Vasan Srinivasan
    • 1
  • Harsh Jhamtani
    • 1
  1. 1.BigData Experience LabAdobe ResearchBangaloreIndia

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