# Enhancing Keyphrase Extraction from Microblogs using Human Reading Time

arXiv: https://arxiv.org/abs/2010.09934 · HTML: https://mlsanity.com/paper/2010.09934/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2020.

## Built on by (most cited first, 6 of 6)

- [Enhancing Keyphrase Extraction from Academic Articles with their Reference Information](https://mlsanity.com/paper/2111.14106/) (2021)
- [Detecting Emerging Technologies and their Evolution using Deep Learning and Weak Signal Analysis](https://mlsanity.com/paper/2205.05449/) (2022)
- [Utilizing Cognitive Signals Generated during Human Reading to Enhance Keyphrase Extraction from Microblogs](https://mlsanity.com/paper/2606.26485/) (2026)
- [Does Attention Mechanism Possess the Feature of Human Reading? A Perspective of Sentiment Classification Task](https://mlsanity.com/paper/2209.03557/) (2022)
- [Building a Multimodal Dataset of Academic Paper for Keyword Extraction](https://mlsanity.com/paper/2606.31069/) (2026)
- [Leveraging Human Reading Behavior for Keyphrase Extraction: A Webcam-based Eye-tracking Corpus](https://mlsanity.com/paper/2608.10688/) (2026)

## Abstract (arXiv)

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.