@inproceedings{weller-etal-2020-dont,
title = "You Don{'}t Have Time to Read This: An Exploration of Document Reading Time Prediction",
author = "Weller, Orion and
Hildebrandt, Jordan and
Reznik, Ilya and
Challis, Christopher and
Tass, E. Shannon and
Snell, Quinn and
Seppi, Kevin",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.162",
doi = "10.18653/v1/2020.acl-main.162",
pages = "1789--1794",
abstract = "Predicting reading time has been a subject of much previous work, focusing on how different words affect human processing, measured by reading time. However, previous work has dealt with a limited number of participants as well as word level only predictions (i.e. predicting the time to read a single word). We seek to extend these works by examining whether or not document level predictions are effective, given additional information such as subject matter, font characteristics, and readability metrics. We perform a novel experiment to examine how different features of text contribute to the time it takes to read, distributing and collecting data from over a thousand participants. We then employ a large number of machine learning methods to predict a user{'}s reading time. We find that despite extensive research showing that word level reading time can be most effectively predicted by neural networks, larger scale text can be easily and most accurately predicted by one factor, the number of words.",
}
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%0 Conference Proceedings
%T You Don’t Have Time to Read This: An Exploration of Document Reading Time Prediction
%A Weller, Orion
%A Hildebrandt, Jordan
%A Reznik, Ilya
%A Challis, Christopher
%A Tass, E. Shannon
%A Snell, Quinn
%A Seppi, Kevin
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F weller-etal-2020-dont
%X Predicting reading time has been a subject of much previous work, focusing on how different words affect human processing, measured by reading time. However, previous work has dealt with a limited number of participants as well as word level only predictions (i.e. predicting the time to read a single word). We seek to extend these works by examining whether or not document level predictions are effective, given additional information such as subject matter, font characteristics, and readability metrics. We perform a novel experiment to examine how different features of text contribute to the time it takes to read, distributing and collecting data from over a thousand participants. We then employ a large number of machine learning methods to predict a user’s reading time. We find that despite extensive research showing that word level reading time can be most effectively predicted by neural networks, larger scale text can be easily and most accurately predicted by one factor, the number of words.
%R 10.18653/v1/2020.acl-main.162
%U https://aclanthology.org/2020.acl-main.162
%U https://doi.org/10.18653/v1/2020.acl-main.162
%P 1789-1794
Markdown (Informal)
[You Don’t Have Time to Read This: An Exploration of Document Reading Time Prediction](https://aclanthology.org/2020.acl-main.162) (Weller et al., ACL 2020)
ACL