Text-to-text generation for question answering

W.E. Bosma, Erwin Marsi, Emiel Krahmer, Mariet Theune

    Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

    Abstract

    In this chapter, we describe our efforts in text-to-text generation within the IMOGEN project. In particular, we describe two focus areas of research to improve the quality of the answer: (a) graph-based content selection to improve the answer in terms of usefulness, and (b) sentence fusion to improve the answer in terms of formulation. We use sentence fusion to join together multiple sentences in order to eliminate overlapping parts, thereby reducing redundancy. The results of this work have been applied in the IMIX system. This system uses a question answering system to pinpoint fragments of text which are relevant to the information need expressed by the user. A content selection system then uses these fragments as entry points in the text to formulate a more complete answer. Sentence fusion is applied to manipulate the result in order to increase the fluency of the text.
    Original languageUndefined
    Title of host publicationInteractive Multi-modal Question-Answering
    EditorsAntal van den Bosch, Gosse Bouma
    Place of PublicationBerlin
    PublisherSpringer
    Pages117-145
    Number of pages29
    ISBN (Print)978-3-642-17524-4
    DOIs
    Publication statusPublished - 2011

    Publication series

    NameTheory and Applications of Natural Language Processing
    PublisherSpringer Verlag
    ISSN (Print)2192-032X

    Keywords

    • IR-78522
    • EWI-20800
    • METIS-281563

    Cite this

    Bosma, W. E., Marsi, E., Krahmer, E., & Theune, M. (2011). Text-to-text generation for question answering. In A. van den Bosch, & G. Bouma (Eds.), Interactive Multi-modal Question-Answering (pp. 117-145). (Theory and Applications of Natural Language Processing). Berlin: Springer. https://doi.org/10.1007/978-3-642-17525-1_6
    Bosma, W.E. ; Marsi, Erwin ; Krahmer, Emiel ; Theune, Mariet. / Text-to-text generation for question answering. Interactive Multi-modal Question-Answering. editor / Antal van den Bosch ; Gosse Bouma. Berlin : Springer, 2011. pp. 117-145 (Theory and Applications of Natural Language Processing).
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    abstract = "In this chapter, we describe our efforts in text-to-text generation within the IMOGEN project. In particular, we describe two focus areas of research to improve the quality of the answer: (a) graph-based content selection to improve the answer in terms of usefulness, and (b) sentence fusion to improve the answer in terms of formulation. We use sentence fusion to join together multiple sentences in order to eliminate overlapping parts, thereby reducing redundancy. The results of this work have been applied in the IMIX system. This system uses a question answering system to pinpoint fragments of text which are relevant to the information need expressed by the user. A content selection system then uses these fragments as entry points in the text to formulate a more complete answer. Sentence fusion is applied to manipulate the result in order to increase the fluency of the text.",
    keywords = "IR-78522, EWI-20800, METIS-281563",
    author = "W.E. Bosma and Erwin Marsi and Emiel Krahmer and Mariet Theune",
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    year = "2011",
    doi = "10.1007/978-3-642-17525-1_6",
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    isbn = "978-3-642-17524-4",
    series = "Theory and Applications of Natural Language Processing",
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    booktitle = "Interactive Multi-modal Question-Answering",

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    Bosma, WE, Marsi, E, Krahmer, E & Theune, M 2011, Text-to-text generation for question answering. in A van den Bosch & G Bouma (eds), Interactive Multi-modal Question-Answering. Theory and Applications of Natural Language Processing, Springer, Berlin, pp. 117-145. https://doi.org/10.1007/978-3-642-17525-1_6

    Text-to-text generation for question answering. / Bosma, W.E.; Marsi, Erwin; Krahmer, Emiel; Theune, Mariet.

    Interactive Multi-modal Question-Answering. ed. / Antal van den Bosch; Gosse Bouma. Berlin : Springer, 2011. p. 117-145 (Theory and Applications of Natural Language Processing).

    Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

    TY - CHAP

    T1 - Text-to-text generation for question answering

    AU - Bosma, W.E.

    AU - Marsi, Erwin

    AU - Krahmer, Emiel

    AU - Theune, Mariet

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    PY - 2011

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    N2 - In this chapter, we describe our efforts in text-to-text generation within the IMOGEN project. In particular, we describe two focus areas of research to improve the quality of the answer: (a) graph-based content selection to improve the answer in terms of usefulness, and (b) sentence fusion to improve the answer in terms of formulation. We use sentence fusion to join together multiple sentences in order to eliminate overlapping parts, thereby reducing redundancy. The results of this work have been applied in the IMIX system. This system uses a question answering system to pinpoint fragments of text which are relevant to the information need expressed by the user. A content selection system then uses these fragments as entry points in the text to formulate a more complete answer. Sentence fusion is applied to manipulate the result in order to increase the fluency of the text.

    AB - In this chapter, we describe our efforts in text-to-text generation within the IMOGEN project. In particular, we describe two focus areas of research to improve the quality of the answer: (a) graph-based content selection to improve the answer in terms of usefulness, and (b) sentence fusion to improve the answer in terms of formulation. We use sentence fusion to join together multiple sentences in order to eliminate overlapping parts, thereby reducing redundancy. The results of this work have been applied in the IMIX system. This system uses a question answering system to pinpoint fragments of text which are relevant to the information need expressed by the user. A content selection system then uses these fragments as entry points in the text to formulate a more complete answer. Sentence fusion is applied to manipulate the result in order to increase the fluency of the text.

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    Bosma WE, Marsi E, Krahmer E, Theune M. Text-to-text generation for question answering. In van den Bosch A, Bouma G, editors, Interactive Multi-modal Question-Answering. Berlin: Springer. 2011. p. 117-145. (Theory and Applications of Natural Language Processing). https://doi.org/10.1007/978-3-642-17525-1_6