Keyphrase extraction aims at automatically extracting a list of "important"
phrases representing the key concepts in a document. Prior approaches for
unsupervised keyphrase extraction resorted to heuristic notions of phrase
importance via embedding clustering or graph centrality, requiring extensive
domain expertise. Our work presents a simple alternative approach which defines
keyphrases as document phrases that are salient for predicting the topic of the
document. To this end, we propose INSPECT -- an approach that uses
self-explaining models for identifying influential keyphrases in a document by
measuring the predictive impact of input phrases on the downstream task of the
document topic classification. We show that this novel method not only
alleviates the need for ad-hoc heuristics but also achieves state-of-the-art
results in unsupervised keyphrase extraction in four datasets across two
domains: scientific publications and news articles.

本文提出了一种基于预测文档主题的显著性测量方法 INSPECT，用于自动提取关键短语，取代了需要领域专业知识的启发式短语重要性聚类或图中心性方法，并在科学出版物和新闻文章两个领域的四个数据集中取得了最先进的无监督关键短语提取结果。