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Section mixture models for scientific document summarization

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Abstract

In this paper, we present a system for summarization of scientific and structured documents that has three components: section mixture models are used for estimation of the weights of terms; a hypothesis test to select a subset of these terms; and a sentence extractor based on techniques for combinatorial optimization. The section mixture models approach is an adaptation of a bigram mixture model based on the main sections of a scientific document and a collection of citing sentences (citances) from papers that reference the document. The model was adapted from earlier work done on Biomedical documents used in the summarization task of the 2014 Text Analysis Conference (TAC 2014). The mixture model trained on the Biomedical data was used also on the data for the Computational Linguistics scientific summarization task of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (CL-SciSumm 2016). This model gives rise to machine-generated summaries with ROUGE scores that are nearly as strong as those seen on the Biomedical data and was also the highest scoring submission to the task of generating a human summary. For sentence extraction, we use the OCCAMS algorithm (Davis et al., in: Vreeken, Ling, Zaki, Siebes, Yu, Goethals, Webb, Wu (eds) ICDM workshops, IEEE Computer Society, pp 454–463, 2012) which takes the sentences from the original document and the assignment of weights of the terms computed by the language models and outputs a set of minimally overlapping sentences whose combined term coverage is maximized. Finally, we explore the importance of an appropriate background model for the hypothesis test to select terms to achieve the best quality summaries.

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Notes

  1. See https://tac.nist.gov/2014/BiomedSumm/guidelines.htmlfordetailedinformation.

  2. https://catalog.ldc.upenn.edu.

  3. An excellent overview of the paper by Ted Dunning can be accessed at http://tdunning.blogspot.com/2008/03/surprise-and-coincidence.html.

  4. https://www.ldc.upenn.edu.

  5. Figure derived from [2] with permission of author.

  6. http://www.rxnlp.com/rouge-2-0.

  7. https://github.com/chbrown/acl-anthology-network.

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Acknowledgements

The authors would like to thank Jeff Kubina for graciously gathering the background corpus for the Biomedical literature summarization task. In addition, extreme thanks to the anonymous reviewers for their careful and critical reviews! Their suggestions of changes as well as the additional experiments using differing background corpus as well as comparison to additional baselines greatly improved this work.

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Authors and Affiliations

  1. IDA Center for Computing Sciences, 17100 Science Drive, Bowie, MD, 20715, USA

    John M. Conroy & Sashka T. Davis

  2. RSA Security, Reston, VA, USA

    Sashka T. Davis

Authors
  1. John M. Conroy
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  2. Sashka T. Davis
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Correspondence to John M. Conroy.

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Section 4 is based on [6] Copyright ©2015, Association for Computational Linguistics. All rights reserved. Reprinted by permission of ACL and the authors.

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Conroy, J.M., Davis, S.T. Section mixture models for scientific document summarization. Int J Digit Libr 19, 305–322 (2018). https://doi.org/10.1007/s00799-017-0218-6

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