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
Dans le domaine de la synthèse de texte, une table des matières est un type de résumé indicatif particulièrement adapté pour localiser des informations dans un long document ou un ensemble de documents. C'est également un résumé utile pour qu'un lecteur puisse obtenir rapidement un aperçu de l'ensemble du contenu. Les modèles actuels de génération d'une table des matières produisent une sortie de qualité relativement faible avec de nombreux titres dénués de sens, ou des titres qui n'ont aucune signification superposée avec le contenu correspondant. Ce problème peut être dû au manque d’informations sémantiques et thématiques dans ces modèles. Dans cette recherche, nous proposons d'intégrer des connaissances de soutien dans les modèles d'apprentissage pour améliorer la qualité des titres dans une table des matières générée. Les connaissances de soutien sont dérivées d'un regroupement hiérarchique de mots, construit à partir d'une large collection de textes bruts, et d'un modèle thématique, directement estimé à partir des données d'entraînement. Les résultats relativement bons des expériences ont montré que les informations sémantiques et thématiques fournies par les connaissances de support ont de bons effets sur la génération de titres et, par conséquent, contribuent à améliorer la qualité de la table des matières générée.
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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
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@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},}
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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 -