Neural keyphrase generation models have recently attracted much interest due to their ability to output absent keyphrases, that is, keyphrases that do not appear in the source text. In this paper, we discuss the usefulness of absent keyphrases from an Information Retrieval (IR) perspective, and show that the commonly drawn distinction between present and absent keyphrases is not made explicit enough. We introduce a finer-grained categorization scheme that sheds more light on the impact of absent keyphrases on scientific document retrieval. Under this scheme, we find that only a fraction (around 20%) of the words that make up keyphrases actually serves as document expansion, but that this small fraction of words is behind much of the gains observed in retrieval effectiveness. We also discuss how the proposed scheme can offer a new angle to evaluate the output of neural keyphrase generation models.

本文讨论了神经关键词生成模型的实用性及其对信息检索的影响，引入了一种细分的分类方案，证明只有关键词中的很少一部分起到了文档扩展的作用，但这一小部分却是检索有效性提高的主要原因。同时，提出的方案可以作为一种新的角度来评估神经关键词生成模型的输出。

重新定义缺失关键短语及其对检索效果的影响