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        <title>A topic-based sentence representation for extractive text summarization</title>
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        <namePart type="given">Nikolaos</namePart>
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            <title>Proceedings of the Workshop MultiLing 2019: Summarization Across Languages, Genres and Sources</title>
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            <namePart type="given">George</namePart>
            <namePart type="family">Giannakopoulos</namePart>
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    <abstract>In this study, we examine the effect of probabilistic topic model-based word representations, on sentence-based extractive summarization. We formulate the task of summary extraction as a binary classification problem, and we test a variety of machine learning algorithms, exploring a range of different settings. An wide experimental evaluation on the MultiLing 2015 MSS dataset illustrates that topic-based representations can prove beneficial to the extractive summarization process in terms of F1, ROUGE-L and ROUGE-W scores, compared to a TF-IDF baseline, with QDA-based analysis providing the best results.</abstract>
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%0 Conference Proceedings
%T A topic-based sentence representation for extractive text summarization
%A Gialitsis, Nikolaos
%A Pittaras, Nikiforos
%A Stamatopoulos, Panagiotis
%Y Giannakopoulos, George
%S Proceedings of the Workshop MultiLing 2019: Summarization Across Languages, Genres and Sources
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F gialitsis-etal-2019-topic
%X In this study, we examine the effect of probabilistic topic model-based word representations, on sentence-based extractive summarization. We formulate the task of summary extraction as a binary classification problem, and we test a variety of machine learning algorithms, exploring a range of different settings. An wide experimental evaluation on the MultiLing 2015 MSS dataset illustrates that topic-based representations can prove beneficial to the extractive summarization process in terms of F1, ROUGE-L and ROUGE-W scores, compared to a TF-IDF baseline, with QDA-based analysis providing the best results.
%R 10.26615/978-954-452-058-8_005
%U https://aclanthology.org/W19-8905/
%U https://doi.org/10.26615/978-954-452-058-8_005
%P 26-34

Markdown (Informal)

[A topic-based sentence representation for extractive text summarization](https://aclanthology.org/W19-8905/) (Gialitsis et al., RANLP 2019)

• A topic-based sentence representation for extractive text summarization (Gialitsis et al., RANLP 2019)

ACL

• Nikolaos Gialitsis, Nikiforos Pittaras, and Panagiotis Stamatopoulos. 2019. A topic-based sentence representation for extractive text summarization. In Proceedings of the Workshop MultiLing 2019: Summarization Across Languages, Genres and Sources, pages 26–34, Varna, Bulgaria. INCOMA Ltd..