Code and dataset for our paper, 'Towards Proactively Forecasting Sentence-Specific Information Popularity within Online News Documents' at the 33rd ACM Conference on Hypertext and Social Media (HT '22)
- InfoPop dataset with train, validation, and test splits can be found here.
BibTeX to cite our work:
@inproceedings{10.1145/3511095.3531268,
author = {Ghosh Roy, Sayar and Padhi, Anshul and Jain, Risubh and Gupta, Manish and Varma, Vasudeva},
title = {Towards Proactively Forecasting Sentence-Specific Information Popularity within Online News Documents},
year = {2022},
isbn = {9781450392334},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3511095.3531268},
doi = {10.1145/3511095.3531268},
booktitle = {Proceedings of the 33rd ACM Conference on Hypertext and Social Media},
pages = {11–20},
numpages = {10},
keywords = {Sentence Popularity Forecasting, Supervised Transfer Learning, Sentence Salience Prediction},
location = {Barcelona, Spain},
series = {HT '22}
}