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Leveraging Content Similarity in Summaries for Generating Better Ensembles

  • Parth MehtaEmail author
  • Prasenjit Majumder
Chapter

Abstract

Previously in Chap.  5 we described the technique to effectively aggregate rank lists by variation in sentence similarity, text representation and ranking algorithms. This was part of a larger family of Consensus-based summarisation systems, that democratically select common content from several candidate systems by taking into account the individual rankings of candidates. In this chapter, we highlight the significant limitations of consensus-based systems that rely only on sentence ranking and not on the actual content of the candidate summaries. Their inability to take into account relative performance of individual systems and overlooking content of candidate summaries in favour of the sentence rankings limits their performance in several cases. We suggest an alternate approach that can potentially overcome these limitations. We show how, in the absence of gold standard summaries, the candidates can act as pseudo-relevant summaries to estimate the performance of individual systems. We then use this information to generate a better aggregate. Experiments show that the proposed content-based aggregation system outperforms existing rank list based aggregation techniques by a large margin.

Notes

Acknowledgements

Adapted/Translated by permission from Springer Nature: Springer Nature, Advances in Information Retrieval, pages no. 787–793, Content Based Weighted Consensus Summarization, Parth Mehta and Prasenjit Majumder, Copyright (2018)

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Information Retrieval and Language Processing LabDhirubhai Ambani Institute of Information and Communication TechnologyGandhinagarIndia

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