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        <title>Predicting sentential semantic compatibility for aggregation in text-to-text generation</title>
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    <name type="personal">
        <namePart type="given">Victor</namePart>
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        <namePart type="given">Jackie</namePart>
        <namePart type="given">Chi</namePart>
        <namePart type="given">Kit</namePart>
        <namePart type="family">Cheung</namePart>
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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>
            <namePart type="family">Prasad</namePart>
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    <abstract>We examine the task of aggregation in the context of text-to-text generation. We introduce a new aggregation task which frames the process as grouping input sentence fragments into clusters that are to be expressed as a single output sentence. We extract datasets for this task from a corpus using an automatic extraction process. Based on the results of a user study, we develop two gold-standard clusterings and corresponding evaluation methods for each dataset. We present a hierarchical clustering framework for predicting aggregation decisions on this task, which outperforms several baselines and can serve as a reference in future work.</abstract>
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        <url>https://aclanthology.org/C16-1101</url>
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%0 Conference Proceedings
%T Predicting sentential semantic compatibility for aggregation in text-to-text generation
%A Chenal, Victor
%A Cheung, Jackie Chi Kit
%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 chenal-cheung-2016-predicting
%X We examine the task of aggregation in the context of text-to-text generation. We introduce a new aggregation task which frames the process as grouping input sentence fragments into clusters that are to be expressed as a single output sentence. We extract datasets for this task from a corpus using an automatic extraction process. Based on the results of a user study, we develop two gold-standard clusterings and corresponding evaluation methods for each dataset. We present a hierarchical clustering framework for predicting aggregation decisions on this task, which outperforms several baselines and can serve as a reference in future work.
%U https://aclanthology.org/C16-1101
%P 1061-1070

Markdown (Informal)

[Predicting sentential semantic compatibility for aggregation in text-to-text generation](https://aclanthology.org/C16-1101) (Chenal & Cheung, COLING 2016)

• Predicting sentential semantic compatibility for aggregation in text-to-text generation (Chenal & Cheung, COLING 2016)

ACL

• Victor Chenal and Jackie Chi Kit Cheung. 2016. Predicting sentential semantic compatibility for aggregation in text-to-text generation. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1061–1070, Osaka, Japan. The COLING 2016 Organizing Committee.