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        <title>Exploring Differential Topic Models for Comparative Summarization of Scientific Papers</title>
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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        <name type="personal">
            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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            <namePart type="given">Rashmi</namePart>
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    <abstract>This paper investigates differential topic models (dTM) for summarizing the differences among document groups. Starting from a simple probabilistic generative model, we propose dTM-SAGE that explicitly models the deviations on group-specific word distributions to indicate how words are used differen-tially across different document groups from a background word distribution. It is more effective to capture unique characteristics for comparing document groups. To generate dTM-based comparative summaries, we propose two sentence scoring methods for measuring the sentence discriminative capacity. Experimental results on scientific papers dataset show that our dTM-based comparative summari-zation methods significantly outperform the generic baselines and the state-of-the-art comparative summarization methods under ROUGE metrics.</abstract>
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        <url>https://aclanthology.org/C16-1098/</url>
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%0 Conference Proceedings
%T Exploring Differential Topic Models for Comparative Summarization of Scientific Papers
%A He, Lei
%A Li, Wei
%A Zhuge, Hai
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F he-etal-2016-exploring
%X This paper investigates differential topic models (dTM) for summarizing the differences among document groups. Starting from a simple probabilistic generative model, we propose dTM-SAGE that explicitly models the deviations on group-specific word distributions to indicate how words are used differen-tially across different document groups from a background word distribution. It is more effective to capture unique characteristics for comparing document groups. To generate dTM-based comparative summaries, we propose two sentence scoring methods for measuring the sentence discriminative capacity. Experimental results on scientific papers dataset show that our dTM-based comparative summari-zation methods significantly outperform the generic baselines and the state-of-the-art comparative summarization methods under ROUGE metrics.
%U https://aclanthology.org/C16-1098/
%P 1028-1038

Markdown (Informal)

[Exploring Differential Topic Models for Comparative Summarization of Scientific Papers](https://aclanthology.org/C16-1098/) (He et al., COLING 2016)

• Exploring Differential Topic Models for Comparative Summarization of Scientific Papers (He et al., COLING 2016)

ACL

• Lei He, Wei Li, and Hai Zhuge. 2016. Exploring Differential Topic Models for Comparative Summarization of Scientific Papers. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1028–1038, Osaka, Japan. The COLING 2016 Organizing Committee.