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Towards Generating Text Summaries for Entity Chains

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Advances in Information Retrieval (ECIR 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8416))

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Abstract

Given a large knowledge graph, discovering meaningful relationships between a given pair of entities has gained a lot of attention in the recent times. Most existing algorithms focus their attention on identifying one or more structures –such as relationship chains or subgraphs– between the entities. The burden of interpreting these results, after combining with contextual information and description of relationships, lies with the user. In this paper, we present a framework that eases this burden by generating a textual summary which incorporates the context and description of individual (dyadic) relationships, and combines them to generate a ranked list of summaries. We develop a model that captures key properties of a well-written text, such as coherence and information content. We focus our attention on a special class of relationship structures, two-length entity chains, and show that the generated ranked list of summaries have 79% precision at rank-1. Our results demonstrate that the generated summaries are quite useful to users.

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Author information

Authors and Affiliations

  1. Indraprastha Institute of Information Technology, New Delhi, India

    Shruti Chhabra & Srikanta Bedathur

Authors
  1. Shruti Chhabra
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  2. Srikanta Bedathur
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Editor information

Editors and Affiliations

  1. University of Amsterdam, Amsterdam, The Netherlands

    Maarten de Rijke  & Tom Kenter  & 

  2. Centrum Wiskunde en Informatica, Amsterdam, The Netherlands and Delft University of Technology, Delft, The Netherlands

    Arjen P. de Vries

  3. University of Illinois at Urbana-Champaign, Urbana, IL, USA

    ChengXiang Zhai

  4. University of Twente, Twente, The Netheralnds and Erasmus University Rotterdam, Rotterdam, The Netherlands

    Franciska de Jong

  5. SalesPredict, Haifa, Israel

    Kira Radinsky

  6. Microsoft Research, Cambridge, UK

    Katja Hofmann

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© 2014 Springer International Publishing Switzerland

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Chhabra, S., Bedathur, S. (2014). Towards Generating Text Summaries for Entity Chains. In: de Rijke, M., et al. Advances in Information Retrieval. ECIR 2014. Lecture Notes in Computer Science, vol 8416. Springer, Cham. https://doi.org/10.1007/978-3-319-06028-6_12

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  • DOI: https://doi.org/10.1007/978-3-319-06028-6_12

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06027-9

  • Online ISBN: 978-3-319-06028-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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