  The premise of manual keyphrase annotation is to read the corresponding
content of an annotated object. Intuitively, when we read, more important words
will occupy a longer reading time. Hence, by leveraging human reading time, we
can find the salient words in the corresponding content. However, previous
studies on keyphrase extraction ignore human reading features. In this article,
we aim to leverage human reading time to extract keyphrases from microblog
posts. There are two main tasks in this study. One is to determine how to
measure the time spent by a human on reading a word. We use eye fixation
durations extracted from an open source eye-tracking corpus (OSEC). Moreover,
we propose strategies to make eye fixation duration more effective on keyphrase
extraction. The other task is to determine how to integrate human reading time
into keyphrase extraction models. We propose two novel neural network models.
The first is a model in which the human reading time is used as the ground
truth of the attention mechanism. In the second model, we use human reading
time as the external feature. Quantitative and qualitative experiments show
that our proposed models yield better performance than the baseline models on
two microblog datasets.
