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        <title>Generation of Company descriptions using concept-to-text and text-to-text deep models: dataset collection and systems evaluation</title>
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    <name type="personal">
        <namePart type="given">Raheel</namePart>
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    <name type="personal">
        <namePart type="given">Khoder</namePart>
        <namePart type="family">Jneid</namePart>
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    <name type="personal">
        <namePart type="given">François</namePart>
        <namePart type="family">Portet</namePart>
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    <name type="personal">
        <namePart type="given">Cyril</namePart>
        <namePart type="family">Labbé</namePart>
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            <title>Proceedings of the 11th International Conference on Natural Language Generation</title>
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            <namePart type="given">Emiel</namePart>
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        <name type="personal">
            <namePart type="given">Albert</namePart>
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            <namePart type="given">Martijn</namePart>
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    <abstract>In this paper we study the performance of several state-of-the-art sequence-to-sequence models applied to generation of short company descriptions. The models are evaluated on a newly created and publicly available company dataset that has been collected from Wikipedia. The dataset consists of around 51K company descriptions that can be used for both concept-to-text and text-to-text generation tasks. Automatic metrics and human evaluation scores computed on the generated company descriptions show promising results despite the difficulty of the task as the dataset (like most available datasets) has not been originally designed for machine learning. In addition, we perform correlation analysis between automatic metrics and human evaluations and show that certain automatic metrics are more correlated to human judgments.</abstract>
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    <identifier type="doi">10.18653/v1/W18-6532</identifier>
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        <date>2018-11</date>
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%0 Conference Proceedings
%T Generation of Company descriptions using concept-to-text and text-to-text deep models: dataset collection and systems evaluation
%A Qader, Raheel
%A Jneid, Khoder
%A Portet, François
%A Labbé, Cyril
%Y Krahmer, Emiel
%Y Gatt, Albert
%Y Goudbeek, Martijn
%S Proceedings of the 11th International Conference on Natural Language Generation
%D 2018
%8 November
%I Association for Computational Linguistics
%C Tilburg University, The Netherlands
%F qader-etal-2018-generation
%X In this paper we study the performance of several state-of-the-art sequence-to-sequence models applied to generation of short company descriptions. The models are evaluated on a newly created and publicly available company dataset that has been collected from Wikipedia. The dataset consists of around 51K company descriptions that can be used for both concept-to-text and text-to-text generation tasks. Automatic metrics and human evaluation scores computed on the generated company descriptions show promising results despite the difficulty of the task as the dataset (like most available datasets) has not been originally designed for machine learning. In addition, we perform correlation analysis between automatic metrics and human evaluations and show that certain automatic metrics are more correlated to human judgments.
%R 10.18653/v1/W18-6532
%U https://aclanthology.org/W18-6532
%U https://doi.org/10.18653/v1/W18-6532
%P 254-263

Markdown (Informal)

[Generation of Company descriptions using concept-to-text and text-to-text deep models: dataset collection and systems evaluation](https://aclanthology.org/W18-6532) (Qader et al., INLG 2018)

• Generation of Company descriptions using concept-to-text and text-to-text deep models: dataset collection and systems evaluation (Qader et al., INLG 2018)

ACL

• Raheel Qader, Khoder Jneid, François Portet, and Cyril Labbé. 2018. Generation of Company descriptions using concept-to-text and text-to-text deep models: dataset collection and systems evaluation. In Proceedings of the 11th International Conference on Natural Language Generation, pages 254–263, Tilburg University, The Netherlands. Association for Computational Linguistics.