@inproceedings{chert-etal-1998-ntu,
title = "An {NTU}-Approach to Automatic Sentence Extraction for Summary Generation",
author = "Chen, Kuang-hua and
Huang, Sheng-Jie and
Lin, Wen-Cheng and
Chen, Hsin-Hsi",
booktitle = "TIPSTER TEXT PROGRAM PHASE III: Proceedings of a Workshop held at Baltimore, {M}aryland, October 13-15, 1998",
month = oct,
year = "1998",
address = "Baltimore, Maryland, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/X98-1022/",
doi = "10.3115/1119089.1119117",
pages = "163--170",
abstract = "Automatic summarization and information extraction are two important Internet services. MUC and SUMMAC play their appropriate roles in the next generation Internet. This paper focuses on the automatic summarization and proposes two different models to extract sentences for summary generation under two tasks initiated by SUMMAC-1. For categorization task, positive feature vectors and negative feature vectors are used cooperatively to construct generic, indicative summaries. For adhoc task, a text model based on relationship between nouns and verbs is used to filter out irrelevant discourse segment, to rank relevant sentences, and to generate the user-directed summaries. The result shows that the NormF of the best summary and that of the fixed summary for adhoc tasks are 0.456 and 0.447. The NormF of the best summary and that of the fixed summary for categorization task are 0.4090 and 0.4023. Our system outperforms the average system in categorization task but does a common job in adhoc task."
}
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<abstract>Automatic summarization and information extraction are two important Internet services. MUC and SUMMAC play their appropriate roles in the next generation Internet. This paper focuses on the automatic summarization and proposes two different models to extract sentences for summary generation under two tasks initiated by SUMMAC-1. For categorization task, positive feature vectors and negative feature vectors are used cooperatively to construct generic, indicative summaries. For adhoc task, a text model based on relationship between nouns and verbs is used to filter out irrelevant discourse segment, to rank relevant sentences, and to generate the user-directed summaries. The result shows that the NormF of the best summary and that of the fixed summary for adhoc tasks are 0.456 and 0.447. The NormF of the best summary and that of the fixed summary for categorization task are 0.4090 and 0.4023. Our system outperforms the average system in categorization task but does a common job in adhoc task.</abstract>
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%0 Conference Proceedings
%T An NTU-Approach to Automatic Sentence Extraction for Summary Generation
%A Chen, Kuang-hua
%A Huang, Sheng-Jie
%A Lin, Wen-Cheng
%A Chen, Hsin-Hsi
%S TIPSTER TEXT PROGRAM PHASE III: Proceedings of a Workshop held at Baltimore, Maryland, October 13-15, 1998
%D 1998
%8 October
%I Association for Computational Linguistics
%C Baltimore, Maryland, USA
%F chert-etal-1998-ntu
%X Automatic summarization and information extraction are two important Internet services. MUC and SUMMAC play their appropriate roles in the next generation Internet. This paper focuses on the automatic summarization and proposes two different models to extract sentences for summary generation under two tasks initiated by SUMMAC-1. For categorization task, positive feature vectors and negative feature vectors are used cooperatively to construct generic, indicative summaries. For adhoc task, a text model based on relationship between nouns and verbs is used to filter out irrelevant discourse segment, to rank relevant sentences, and to generate the user-directed summaries. The result shows that the NormF of the best summary and that of the fixed summary for adhoc tasks are 0.456 and 0.447. The NormF of the best summary and that of the fixed summary for categorization task are 0.4090 and 0.4023. Our system outperforms the average system in categorization task but does a common job in adhoc task.
%R 10.3115/1119089.1119117
%U https://aclanthology.org/X98-1022/
%U https://doi.org/10.3115/1119089.1119117
%P 163-170
Markdown (Informal)
[An NTU-Approach to Automatic Sentence Extraction for Summary Generation](https://aclanthology.org/X98-1022/) (Chen et al., TIPSTER 1998)
ACL
- Kuang-hua Chen, Sheng-Jie Huang, Wen-Cheng Lin, and Hsin-Hsi Chen. 1998. An NTU-Approach to Automatic Sentence Extraction for Summary Generation. In TIPSTER TEXT PROGRAM PHASE III: Proceedings of a Workshop held at Baltimore, Maryland, October 13-15, 1998, pages 163–170, Baltimore, Maryland, USA. Association for Computational Linguistics.