Identifying Argument Components through TextRank
Identifying Argument Components through TextRank
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通过 TextRank 识别参数组件
DOI：
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发表时间：
2016
期刊：
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通讯作者：
V. Karkaletsis
中科院分区：
文献类型：
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作者：
G. Petasis;V. Karkaletsis
在本文中，我们研究了无监督提取摘要算法 TextRank 在不同任务（识别论证成分）上的应用。我们的主要动机是检查提取摘要和论点挖掘之间是否存在潜在的重叠，以及摘要中使用的方法（通常将文档建模为一个整体）是否可以对论点挖掘任务产生积极影响。对两个包含来自在线辩论论坛的用户帖子和说服性文章的语料库进行了评估。评估结果表明，基于图的方法和针对提取摘要的方法可以对与论点挖掘相关的任务产生积极影响。
In this paper we examine the application of an unsupervised extractive summarisation algorithm, TextRank, on a different task, the identification of argumentative components. Our main motivation is to examine whether there is any potential overlap between extractive summarisation and argument mining, and whether approaches used in summarisation (which typically model a document as a whole) can have a positive effect on tasks of argument mining. Evaluation has been performed on two corpora containing user posts from an on-line debating forum and persuasive essays. Evaluation results suggest that graph-based approaches and approaches targeting extractive summarisation can have a positive effect on tasks related to argument mining.