The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Dalam medan ringkasan teks, jadual kandungan ialah sejenis ringkasan indikatif yang amat sesuai untuk mencari maklumat dalam dokumen panjang atau satu set dokumen. Ia juga merupakan ringkasan yang berguna untuk pembaca mendapatkan gambaran keseluruhan kandungan dengan cepat. Model semasa untuk menjana jadual kandungan menghasilkan output berkualiti rendah dengan banyak tajuk yang tidak bermakna, atau tajuk yang tidak mempunyai makna bertindih dengan kandungan yang sepadan. Masalah ini mungkin disebabkan oleh kekurangan maklumat semantik dan maklumat topik dalam model tersebut. Dalam penyelidikan ini, kami mencadangkan untuk menyepadukan pengetahuan sokongan ke dalam model pembelajaran untuk meningkatkan kualiti tajuk dalam jadual kandungan yang dihasilkan. Pengetahuan sokongan diperoleh daripada pengelompokan hierarki perkataan, yang dibina daripada koleksi teks mentah yang besar, dan model topik, yang dianggarkan secara langsung daripada data latihan. Keputusan eksperimen yang agak baik menunjukkan bahawa maklumat semantik dan topik yang dibekalkan oleh pengetahuan sokongan mempunyai kesan yang baik terhadap penjanaan tajuk, dan oleh itu, ia membantu meningkatkan kualiti jadual kandungan yang dihasilkan.
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Salinan
Viet Cuong NGUYEN, Le Minh NGUYEN, Akira SHIMAZU, "Learning to Generate a Table-of-Contents with Supportive Knowledge" in IEICE TRANSACTIONS on Information,
vol. E94-D, no. 3, pp. 423-431, March 2011, doi: 10.1587/transinf.E94.D.423.
Abstract: In the text summarization field, a table-of-contents is a type of indicative summary that is especially suited for locating information in a long document, or a set of documents. It is also a useful summary for a reader to quickly get an overview of the entire contents. The current models for generating a table-of-contents produced relatively low quality output with many meaningless titles, or titles that have no overlapping meaning with the corresponding contents. This problem may be due to the lack of semantic information and topic information in those models. In this research, we propose to integrate supportive knowledge into the learning models to improve the quality of titles in a generated table-of-contents. The supportive knowledge is derived from a hierarchical clustering of words, which is built from a large collection of raw text, and a topic model, which is directly estimated from the training data. The relatively good results of the experiments showed that the semantic and topic information supplied by supportive knowledge have good effects on title generation, and therefore, they help to improve the quality of the generated table-of-contents.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E94.D.423/_p
Salinan
@ARTICLE{e94-d_3_423,
author={Viet Cuong NGUYEN, Le Minh NGUYEN, Akira SHIMAZU, },
journal={IEICE TRANSACTIONS on Information},
title={Learning to Generate a Table-of-Contents with Supportive Knowledge},
year={2011},
volume={E94-D},
number={3},
pages={423-431},
abstract={In the text summarization field, a table-of-contents is a type of indicative summary that is especially suited for locating information in a long document, or a set of documents. It is also a useful summary for a reader to quickly get an overview of the entire contents. The current models for generating a table-of-contents produced relatively low quality output with many meaningless titles, or titles that have no overlapping meaning with the corresponding contents. This problem may be due to the lack of semantic information and topic information in those models. In this research, we propose to integrate supportive knowledge into the learning models to improve the quality of titles in a generated table-of-contents. The supportive knowledge is derived from a hierarchical clustering of words, which is built from a large collection of raw text, and a topic model, which is directly estimated from the training data. The relatively good results of the experiments showed that the semantic and topic information supplied by supportive knowledge have good effects on title generation, and therefore, they help to improve the quality of the generated table-of-contents.},
keywords={},
doi={10.1587/transinf.E94.D.423},
ISSN={1745-1361},
month={March},}
Salinan
TY - JOUR
TI - Learning to Generate a Table-of-Contents with Supportive Knowledge
T2 - IEICE TRANSACTIONS on Information
SP - 423
EP - 431
AU - Viet Cuong NGUYEN
AU - Le Minh NGUYEN
AU - Akira SHIMAZU
PY - 2011
DO - 10.1587/transinf.E94.D.423
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2011
AB - In the text summarization field, a table-of-contents is a type of indicative summary that is especially suited for locating information in a long document, or a set of documents. It is also a useful summary for a reader to quickly get an overview of the entire contents. The current models for generating a table-of-contents produced relatively low quality output with many meaningless titles, or titles that have no overlapping meaning with the corresponding contents. This problem may be due to the lack of semantic information and topic information in those models. In this research, we propose to integrate supportive knowledge into the learning models to improve the quality of titles in a generated table-of-contents. The supportive knowledge is derived from a hierarchical clustering of words, which is built from a large collection of raw text, and a topic model, which is directly estimated from the training data. The relatively good results of the experiments showed that the semantic and topic information supplied by supportive knowledge have good effects on title generation, and therefore, they help to improve the quality of the generated table-of-contents.
ER -