Enhancing Keyphrase Extraction from Microblogs using Human Reading Time

TL;DR

This paper introduces novel neural network models that incorporate human reading time, measured via eye fixation durations, to improve keyphrase extraction from microblogs, demonstrating superior performance over baseline models.

Contribution

It is the first to leverage human reading time as a feature in neural keyphrase extraction models for microblogs, using eye-tracking data to enhance model effectiveness.

Findings

Proposed models outperform baseline models in keyphrase extraction accuracy.

Using eye fixation durations improves the identification of salient words.

Human reading time as an external feature enhances model performance.

Abstract

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.