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Multi-document Summarization Based on Atomic Semantic Events and Their Temporal Relationships

  • Conference paper
Advances in Information Retrieval (ECIR 2016)

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

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Abstract

Automatic multi-document summarization (MDS) is the process of extracting the most important information, such as events and entities, from multiple natural language texts focused on the same topic. In this paper, we experiment with the effects of different groups of information such as events and named entities in the domain of generic and update MDS. Our generic MDS system has outperformed the best recent generic MDS systems in DUC 2004 in terms of ROUGE-1 recall and \(f_1\)-measure. Update summarization is a new form of MDS, where novel yet salient sentences are chosen as summary sentences based on the assumption that the user has already read a given set of documents. We present an event based update summarization where the novelty is detected based on the temporal ordering of events, and the saliency is ensured by the event and entity distribution. To our knowledge, no other study has deeply experimented with the effects of the novelty information acquired from the temporal ordering of events (assuming that a sentence contains one or more events) in the domain of update multi-document summarization. Our update MDS system has outperformed the state-of-the-art update MDS system in terms of ROUGE-2 and ROUGE-SU4 recall measures. All our MDS systems also generate quality summaries which are manually evaluated based on popular evaluation criteria.

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Notes

  1. 1.

    Here ‘22 years’ is a time period. Time periods do not carry important information for detecting novelty.

  2. 2.

    http://duc.nist.gov/duc2007/tasks.html.

  3. 3.

    http://nlp.stanford.edu/software/corenlp.shtml.

  4. 4.

    http://code.google.com/p/cleartk/.

  5. 5.

    Document Creation Time (DCT) can be calculated from document name.

  6. 6.

    Total 4 topics are taken into account, i.e. K is 4.

  7. 7.

    ROUGE runtime arguments for DUC 2004:

    \(ROUGE \text{- }a \text{- }c 95 \text{- }b 665 \text{- }m \text{- }n 4 \text{- }w 1.2\).

  8. 8.

    We do not compare our system with the recent topic model based system [14] because that system is significantly outperformed by Lin and Bilmes’s [23] system in terms of both ROUGE-1 recall and \(f_1\)-measure.

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Authors and Affiliations

  1. University of Lethbridge, Lethbridge, AB, Canada

    Yllias Chali & Mohsin Uddin

Authors
  1. Yllias Chali
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  2. Mohsin Uddin
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Correspondence to Yllias Chali .

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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Chali, Y., Uddin, M. (2016). Multi-document Summarization Based on Atomic Semantic Events and Their Temporal Relationships. 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_27

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  • DOI: https://doi.org/10.1007/978-3-319-30671-1_27

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-30670-4

  • Online ISBN: 978-3-319-30671-1

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