Guided summarization for Indonesian news articles

2014 International Conference of Advanced Informatics: Concept, Theory and Application (ICAICTA) Pub Date : 2014-08-01 DOI:10.1109/ICAICTA.2014.7005930

Danang Tri Massandy, M. L. Khodra

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引用次数: 5

Abstract

The development of online news media grew in number in Indonesia. One technique of news articles summarization is guided summarization where the summary should contain important aspect information. Guided summarization techniques have been developed in the Text Analysis Conference (TAC) 2011 and one of the best methods is SWING by Jun-ping, et al. The purpose of this study is to adapt the methods of SWING system to Indonesian news articles as well as integrating with News Aggregator system. In this research, the experiments have purpose to determine the best features and system configuration when adapted to Indonesian news articles. ROUGE-2 and ROUGE-SU4 is used to evaluate the results of the summary where a summary of the system results compared to the human-made summaries. The best system configuration produces summary with evaluation of ROUGE-2 0,31 and ROUGE-SU4 0,22 which is very close to the human-made summaries with a value of ROUGE-2 0,32 and ROUGE-SU4 0.24. In addition, the update summarization component can be run by giving a summary of updates without repeating the information. Adaptation from SWING system to Indonesian news articles is employing features such as sentence length (SL), category relevance score (CRS), category KL-Divergence (CKLD), bigram DFS (BDFS), Top n NE corpus, Top n NE topic, quote sentence removal, and building SVR model for each news category.

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印尼新闻文章的指导摘要

印尼的网络新闻媒体发展迅速。新闻文章摘要的一种技巧是引导摘要，其中摘要应该包含重要的方面信息。导读摘要技术在2011年的文本分析会议(TAC)上得到了发展，其中最好的方法之一是Jun-ping等人的SWING。本研究的目的是将SWING系统的方法应用于印尼新闻文章，并与news Aggregator系统进行整合。在本研究中，实验的目的是确定最佳功能和系统配置时，适应印尼新闻文章。ROUGE-2和ROUGE-SU4用于评估总结的结果，其中将系统结果的总结与人工总结进行比较。最佳系统配置生成的总结值为ROUGE-2 0,31和ROUGE-SU4 0,22，与人工总结值ROUGE-2 0,32和ROUGE-SU4 0.24非常接近。此外，可以通过提供更新摘要而不重复信息来运行更新摘要组件。SWING系统对印尼语新闻文章的适应采用了句子长度(SL)、类别相关性评分(CRS)、类别KL-Divergence (CKLD)、重图DFS (BDFS)、Top n NE语料库、Top n NE主题、引用句删除以及为每个新闻类别构建SVR模型等特征。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2014 International Conference of Advanced Informatics: Concept, Theory and Application (ICAICTA)

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