文章基本信息

标题：A Discriminative Sentence Compression Method as Combinatorial Optimization Problem
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作者：Tsutomu Hirao ; Jun Suzuki ; Hideki Isozaki 等
期刊名称：人工知能学会論文誌
印刷版ISSN：1346-0714
电子版ISSN：1346-8030
出版年度：2007
卷号：22
期号：6
页码：574-584
DOI：10.1527/tjsai.22.574
出版社：The Japanese Society for Artificial Intelligence
摘要：In the study of automatic summarization, the main research topic was `important sentence extraction' but nowadays `sentence compression' is a hot research topic. Conventional sentence compression methods usually transform a given sentence into a parse tree or a dependency tree, and modify them to get a shorter sentence. However, this method is sometimes too rigid. In this paper, we regard sentence compression as an combinatorial optimization problem that extracts an optimal subsequence of words. Hori et al. also proposed a similar method, but they used only a small number of features and their weights were tuned by hand. We introduce a large number of features such as part-of-speech bigrams and word position in the sentence. Furthermore, we train the system by discriminative learning. According to our experiments, our method obtained better score than other methods with statistical significance.
关键词：text summarization ; sentence compression ; combinatorial optimization ; discriminative learning ; dynamic programming