Using Argumentative Zones for Extractive Summarization of Scientific Articles
Using Argumentative Zones for Extractive Summarization of Scientific Articles
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发表时间：
2012-12
期刊：
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通讯作者：
Danish Contractor;Yufan Guo;A. Korhonen
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作者：
Danish Contractor;Yufan Guo;A. Korhonen
信息结构是说话人在旧语境中表达新信息的句子结构，它能捕捉到科技文献语篇结构中丰富的语言信息。信息结构已被发现对重要的自然语言处理（NLP）任务有用，例如信息检索和提取。由于科学文章通常遵循一定的话语结构，描述先前的工作，解决的问题，使用的方法等，它也可以用于这些文章的摘要。在这项工作中，我们专注于一个计划的信息结构称为议论分区（AZ），并调查其类别是否可以支持提取文本摘要在科学领域。我们开发了一个摘要系统，使用AZ类别（i）作为特征，（ii）在最后的句子选择过程中。我们直接评估系统，以及使用基于任务的评估。实验结果表明，AZ既能支持全文文摘，又能支持自定义文摘。我们报告了一个统计上显着的改善，总结性能的竞争基线，使用期刊部分标签，而不是AZ信息。中文标题和摘要
Information structure, i.e the way speakers construct sentences to present new information in the context of old, can capture rich linguistic information about the discourse structure of scientific documents. Information structure has been found useful for important Natural Language Processing (NLP) tasks, such as information retrieval and extraction. Since scientific articles typically follow a certain discourse structure describing the prior work, problem being solved, methods used, and so forth, it could also be useful for summarization of these articles. In this work we focus on a scheme of information structure called Argumentative Zoning (AZ), and investigate whether its categories could support extractive text summarization in a scientific domain. We develop a summarization system that uses AZ categories (i) as features and (ii) in the final sentence selection process. We evaluate the system directly as well as using task-based evaluation. The results show that AZ can support both full document and customized summarization. We report a statistically significant improvement in summarization performance against a competitive baseline that uses journal section labels instead of AZ information. TITLE AND ABSTRACT IN MANDARIN