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        <title>Frustratingly Easy Model Ensemble for Abstractive Summarization</title>
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        <namePart type="given">Hayato</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
            <namePart type="family">Riloff</namePart>
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        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Chiang</namePart>
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            <namePart type="given">Jun’ichi</namePart>
            <namePart type="family">Tsujii</namePart>
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    <abstract>Ensemble methods, which combine multiple models at decoding time, are now widely known to be effective for text-generation tasks. However, they generally increase computational costs, and thus, there have been many studies on compressing or distilling ensemble models. In this paper, we propose an alternative, simple but effective unsupervised ensemble method, post-ensemble, that combines multiple models by selecting a majority-like output in post-processing. We theoretically prove that our method is closely related to kernel density estimation based on the von Mises-Fisher kernel. Experimental results on a news-headline-generation task show that the proposed method performs better than the current ensemble methods.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1449</identifier>
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%0 Conference Proceedings
%T Frustratingly Easy Model Ensemble for Abstractive Summarization
%A Kobayashi, Hayato
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F kobayashi-2018-frustratingly
%X Ensemble methods, which combine multiple models at decoding time, are now widely known to be effective for text-generation tasks. However, they generally increase computational costs, and thus, there have been many studies on compressing or distilling ensemble models. In this paper, we propose an alternative, simple but effective unsupervised ensemble method, post-ensemble, that combines multiple models by selecting a majority-like output in post-processing. We theoretically prove that our method is closely related to kernel density estimation based on the von Mises-Fisher kernel. Experimental results on a news-headline-generation task show that the proposed method performs better than the current ensemble methods.
%R 10.18653/v1/D18-1449
%U https://aclanthology.org/D18-1449/
%U https://doi.org/10.18653/v1/D18-1449
%P 4165-4176

Markdown (Informal)

[Frustratingly Easy Model Ensemble for Abstractive Summarization](https://aclanthology.org/D18-1449/) (Kobayashi, EMNLP 2018)

• Frustratingly Easy Model Ensemble for Abstractive Summarization (Kobayashi, EMNLP 2018)

ACL

• Hayato Kobayashi. 2018. Frustratingly Easy Model Ensemble for Abstractive Summarization. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4165–4176, Brussels, Belgium. Association for Computational Linguistics.