为自然语料中的命题重要性设计可量化的标注方法。
Not Worth Mentioning? A Pilot Study on Salient Proposition Annotation
- 基于分级摘要思想定义命题重要性标注任务。
- 在多文体小数据集上验证标注一致性，初步发现与话语单元中心性的关联。
- 适合关注文本重要性评估与话语分析的研究者。
尽管提取式摘要研究已有长期传统，旨在恢复文本中最重要命题，但对自然语料中命题重要性的分级量化仍缺乏工作。本文采用先前命题实体抽取（SEE）研究中的分级摘要式重要性作为度量，并将其适配用于量化命题重要性。我们定义了标注任务，应用于一个小规模多文体数据集，评估了标注者间一致性，并初步探讨了该度量与基于修辞结构理论（RST）的话语单元中心性之间的关系。
原文摘要 · Abstract (English)
Despite a long tradition of work on extractive summarization, which by nature aims to recover the most important propositions in a text, little work has been done on operationalizing graded proposition salience in naturally occurring data. In this paper, we adopt graded summarization-based salience as a metric from previous work on Salient Entity Extraction (SEE) and adapt it to quantify proposition salience. We define the annotation task, apply it to a small multi-genre dataset, evaluate agreement and carry out a preliminary study of the relationship between our metric and notions of discourse unit centrality in discourse parsing following Rhetorical Structure Theory (RST).
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