Quantitative Argument Summarization and beyond: Cross-Domain Key Point Analysis
Quantitative Argument Summarization and beyond: Cross-Domain Key Point Analysis
复制标题
定量论证总结及超越：跨领域关键点分析
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
N. Slonim
中科院分区：
文献类型：
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作者：
Roy Bar;Yoav Kantor;Lilach Eden;Roni Friedman;Dan Lahav;N. Slonim
在总结关于某个主题的观点、论点或意见时，往往不仅需要提取最突出的观点，而且还需要量化其普遍性。传统上，多文档摘要的工作主要集中在创建文本摘要上，缺乏这种定量方面。最近的研究提出了通过将参数映射到专家生成的一小组关键点来总结参数，其中每个关键点的显着性对应于其匹配参数的数量。目前的工作在两个重要方面推进关键点分析：首先，我们开发了一种自动提取关键点的方法，该方法可以实现全自动分析，并显示出与人类专家相当的性能。其次，我们证明了关键点分析的适用性远远超出了论证数据。使用在公开的论证数据集上训练的模型，我们在另外两个领域取得了令人鼓舞的结果：市政调查和用户评论。另一个贡献是深入评估参数到关键点匹配模型，我们大大优于以前的结果。
When summarizing a collection of views, arguments or opinions on some topic, it is often desirable not only to extract the most salient points, but also to quantify their prevalence. Work on multi-document summarization has traditionally focused on creating textual summaries, which lack this quantitative aspect. Recent work has proposed to summarize arguments by mapping them to a small set of expert-generated key points, where the salience of each key point corresponds to the number of its matching arguments. The current work advances key point analysis in two important respects: first, we develop a method for automatic extraction of key points, which enables fully automatic analysis, and is shown to achieve performance comparable to a human expert. Second, we demonstrate that the applicability of key point analysis goes well beyond argumentation data. Using models trained on publicly available argumentation datasets, we achieve promising results in two additional domains: municipal surveys and user reviews. An additional contribution is an in-depth evaluation of argument-to-key point matching models, where we substantially outperform previous results.
DOI：
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发表时间：
2016
期刊：
Proceedings of the Annual Meeting of the Association for Computational Linguistics
影响因子：
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作者：
Egan C.
通讯作者：
Egan C.