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European Conference on Information Retrieval

ECIR 2016: Advances in Information Retrieval pp 3–14Cite as

SoRTESum: A Social Context Framework for Single-Document Summarization

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9626))

Abstract

The combination of web document contents, sentences and users’ comments from social networks provides a viewpoint of a web document towards a special event. This paper proposes a framework named SoRTESum to take advantage of information from Twitter viz. Diversity and reflection of document content to generate high-quality summaries by a novel sentence similarity measurement. The framework first formulates sentences and tweets by recognizing textual entailment (RTE) relation to incorporate social information. Next, they are modeled in a Dual Wing Entailment Graph, which captures the entailment relation to calculate the sentence similarity based on mutual reinforcement information. Finally, important sentences and representative tweets are selected by a ranking algorithm. By incorporating social information, SoRTESum obtained improvements over state-of-the-art unsupervised baselines e.g., Random, SentenceLead, LexRank of 0.51 %–8.8 % of ROUGE-1 and comparable results with strong supervised methods e.g., L2R and CrossL2R trained by RankBoost for single-document summarization.

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Notes

  1. 1.

    http://twitter.com - a microblogging system.

  2. 2.

    The RTE term was kept instead of the similarity because all features were derived from RTE task.

  3. 3.

    http://www1.se.cuhk.edu.hk/~zywei/data/hilightextraction.zip.

  4. 4.

    http://edition.cnn.com.

  5. 5.

    http://www.usatoday.com.

  6. 6.

    http://snowball.tartarus.org/algorithms/porter/stemmer.html.

  7. 7.

    https://pypi.python.org/pypi/sumy/0.3.0.

  8. 8.

    https://people.cs.umass.edu/~vdang/ranklib.html.

  9. 9.

    https://github.com/klb3713/sentence2vec/blob/master/demo.py.

  10. 10.

    https://meta.wikimedia.org/wiki/Data_dump_torrents.

  11. 11.

    http://kavita-ganesan.com/content/rouge-2.0-documentation.

  12. 12.

    http://150.65.242.101:9293.

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Acknowledgment

We would like to thank to Preslav Nakov and Wei Gao for useful discussions and insightful comments on earlier drafts; Chien-Xuan Tran for building the web interface. We also thank to anonymous reviewers for their detailed comments for improving our paper. This work was partly supported by JSPS KAKENHI Grant number 3050941.

Author information

Authors and Affiliations

  1. Japan Advanced Institute of Science and Technology (JAIST), 1-1 Asahidai, Nomi, Ishikawa, 923-1292, Japan

    Minh-Tien Nguyen & Minh-Le Nguyen

  2. Hung Yen University of Technology and Education (UTEHY), Hung Yen, Vietnam

    Minh-Tien Nguyen

Authors
  1. Minh-Tien Nguyen
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  2. Minh-Le Nguyen
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Corresponding author

Correspondence to Minh-Tien Nguyen .

Editor information

Editors and Affiliations

  1. Department of Information Engineering, University of Padua, Padova, Italy

    Nicola Ferro

  2. Faculty of Informatics, University of Lugano (USI), Lugano, Switzerland

    Fabio Crestani

  3. Department of Computer Science, Katholieke Universiteit Leuven, Heverlee, Belgium

    Marie-Francine Moens

  4. Systèmes d’informations, Big Data et Recherche d’Information, Institut de Recherche en Informatique de Toulouse IRIT/équipe SIG, Toulouse Cedex 04, France

    Josiane Mothe

  5. Yahoo! Labs London, London, UK

    Fabrizio Silvestri

  6. Department of Information Engineering, University of Padua, Padova, Italy

    Giorgio Maria Di Nunzio

  7. TU Delft - EWI/ST/WIS, Delft, The Netherlands

    Claudia Hauff

  8. Department of Information Engineering, University of Padua, Padova, Italy

    Gianmaria Silvello

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© 2016 Springer International Publishing Switzerland

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Cite this paper

Nguyen, MT., Nguyen, ML. (2016). SoRTESum: A Social Context Framework for Single-Document Summarization. In: Ferro, N., et al. Advances in Information Retrieval. ECIR 2016. Lecture Notes in Computer Science(), vol 9626. Springer, Cham. https://doi.org/10.1007/978-3-319-30671-1_1

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  • DOI: https://doi.org/10.1007/978-3-319-30671-1_1

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-30670-4

  • Online ISBN: 978-3-319-30671-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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