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    Interactive Hierarchical Quote Extraction for Content Insights

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    Date
    2019
    Author
    Knittel, Johannes ORCID
    Koch, Steffen ORCID
    Ertl, Thomas ORCID
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    Abstract
    This work presents a new approach to visually summarize large micro-document collections such as tweets. We extract frequent patterns of phrases as shortened quotes to present analysts an overview of popular snippets and statements, enabling more specific insights into large text collections compared to keyword-based visualizations. In our hierarchical structure, each quote can be the starting point to extract more fine-grained patterns on a subset of sentences that match the parent pattern. We show that our approach is scalable by applying it to millions of tweets.
    BibTeX
    @inproceedings {10.2312:eurp.20191134,
    booktitle = {EuroVis 2019 - Posters},
    editor = {Madeiras Pereira, João and Raidou, Renata Georgia},
    title = {{Interactive Hierarchical Quote Extraction for Content Insights}},
    author = {Knittel, Johannes and Koch, Steffen and Ertl, Thomas},
    year = {2019},
    publisher = {The Eurographics Association},
    ISBN = {978-3-03868-088-8},
    DOI = {10.2312/eurp.20191134}
    }
    URI
    https://doi.org/10.2312/eurp.20191134
    https://diglib.eg.org:443/handle/10.2312/eurp20191134
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    Eurographics Association copyright © 2013 - 2023 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA