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System Combination for Multi-document Summarization

Kai Hong,Mitchell P. Marcus,A. Nenkova

Published 2015 in Conference on Empirical Methods in Natural Language Processing

ABSTRACT

We present a novel framework of system combination for multi-document summarization. For each input set (input), we generate candidate summaries by combining whole sentences from the summaries generated by different systems. We show that the oracle among these candidates is much better than the summaries that we have combined. We then present a supervised model to select among the candidates. The model relies on a rich set of features that capture content importance from different perspectives. Our model performs better than the systems that we combined based on manual and automatic evaluations. We also achieve very competitive performance on six DUC/TAC datasets, comparable to the state-of-the-art on most datasets.

PUBLICATION RECORD

  • Publication year

    2015

  • Venue

    Conference on Empirical Methods in Natural Language Processing

  • Publication date

    2015-09-01

  • Fields of study

    Computer Science

  • Identifiers
    DOI 10.18653/v1/D15-1011
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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