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    Title: Artificial Intelligence for Automatic Text Summarization
    Authors: Day, Min-Yuh;Chen, Chao-Yu
    Keywords: Artificial Intelligence;Sequence-to-Sequence;Automatic Text Summarization, Long Short-Term Memory;Recurrent Neural Network
    Date: 2018-07-07
    Issue Date: 2018-10-18 12:12:20 (UTC+8)
    Publisher: IEEE
    Abstract: Automatic text summarization has played a critical role in helping people obtain key information from increasing huge data with the advantaged development of technology. In the past, few literatures are related to solve the problem of generating titles (short summaries) by using artificial intelligence (AI). The purpose of this study is that we proposed an AI approach for automatic text summarization. We developed an AI text summarization system architecture with three models, namely, statistical model, machine learning model, and deep learning model as well as evaluating the performance of three models. Essay titles and essay abstracts are used to train artificial intelligence deep learning model to generate the candidate titles and evaluated by ROUGE for performance evaluation. The contribution of this paper is that we proposed an AI automatic text summarization system by applying deep learning to generate short summaries from the titles and abstracts of the Web of Science (WOS) database.
    Relation: Proceedings of the 2018 IEEE 18th International Conference on Information Reuse and Integration (IEEE IRI 2018)
    DOI: https://doi.org/10.1109/IRI.2018.00076
    Appears in Collections:[資訊管理學系暨研究所] 會議論文

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