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Multi-Document Summarization Techniques for Generating Image Descriptions: A Comparative Analysis

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Multi-source, Multilingual Information Extraction and Summarization

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Abstract

This paper reports an initial study that aims to assess the viability of multi-document summarization techniques for automatic captioning of geo-referenced images. The automatic captioning procedure requires summarizing multiple Web documents that contain information related to images’ location. We use different state-of-the art summarization systems to generate generic and query-based multi-document summaries and evaluate them using ROUGE metrics [24] relative to human generated summaries. Results show that query-based summaries perform better than generic ones and thus are more appropriate for the task of image captioning or generation of short descriptions related to the location/place captured in the image. For our future work in automatic image captioning this result suggests that developing the query-based summarizer further and biasing it to account for user-specific requirements will prove worthwhile.

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Notes

  1. 1.

    http://www-nlpir.nist.gov/projects/duc/index.html

  2. 2.

    www.VirtualTourist.com

  3. 3.

    http://search.yahoo.com/

  4. 4.

    http://htmlparser.sourceforge.net/

  5. 5.

    http://www.dcs.shef.ac.uk/~saggion/summa/default.htm

  6. 6.

    http://gate.ac.uk

  7. 7.

    http://www.summarization.com/mead/

  8. 8.

    http://www.d.umn.edu/~tpederse/text-similarity.html

  9. 9.

    http://aye.comp.nus.edu.sg/~qiu/NLPTools/JavaRAP.html

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Acknowledgements

This work was supported by the EU-funded TRIPOD project (IST-FP6-045335) and by the Spanish Government through the FPU program and the projects TIN2009-14659-C03-01, TSI 020312-2009-44, and TIN2009-13391-C04-01; by Conselleria d’Educació – Generalitat Valenciana (grant no. PROMETEO/2009/119 and ACOMP/2010/286); and the FPI program (BES-2007-16268) from the Spanish Ministry of Science and Innovation (project TEXT-MESS (TIN2006-15265-C06-01)). We would like to thank Horacio Saggion for his support with SUMMA. We are also grateful to Emina Kurtic for comments on the previous versions of this paper.

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Authors and Affiliations

  1. University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, UK

    Ahmet Aker & Robert Gaizauskas

  2. Universidad Complutense de Madrid, C/ José García Santesmases, s/n, 28040, Madrid, Spain

    Laura Plaza

  3. University of Alicante, Apdo. de correos, 99, E-03080, Alicante, Spain

    Elena Lloret

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  1. Ahmet Aker
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  4. Robert Gaizauskas
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Correspondence to Ahmet Aker .

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  1. Universite Sorbonne Nouvelle, LATTICE-CNRS, Ecole Normale Superieure and, rue d'Ulm 45, Paris, 75005, France

    Thierry Poibeau

  2. , Information & Communication Technologies, Universitat Pompeu Fabra, C/ Tanger 122-140, Barcelona, 08018, Spain

    Horacio Saggion

  3. Institute for Computer Science, Polish Acadmey of Science, ul. Jana Kazimierza 5, Warsaw, 01-248, Poland

    Jakub Piskorski

  4. Department of Computer Science, University of Helsinki, Gustaf Hällströmin katu 2, Helsinki, 00014, Finland

    Roman Yangarber

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Aker, A., Plaza, L., Lloret, E., Gaizauskas, R. (2013). Multi-Document Summarization Techniques for Generating Image Descriptions: A Comparative Analysis. In: Poibeau, T., Saggion, H., Piskorski, J., Yangarber, R. (eds) Multi-source, Multilingual Information Extraction and Summarization. Theory and Applications of Natural Language Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28569-1_14

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