Distinguishing Between Foreground and Background Events in News
Mohammed Aldawsari, Adrian Perez, Deya Banisakher, Mark Finlayson
Abstract
Determining whether an event in a news article is a foreground or background event would be useful in many natural language processing tasks, for example, temporal relation extraction, summarization, or storyline generation. We introduce the task of distinguishing between foreground and background events in news articles as well as identifying the general temporal position of background events relative to the foreground period (past, present, future, and their combinations). We achieve good performance (0.73 F1 for background vs. foreground and temporal position, and 0.79 F1 for background vs. foreground only) on a dataset of news articles by leveraging discourse information in a featurized model. We release our implementation and annotated data for other researchers
BibTeX
@inproceedings{aldawsari-etal-2020-distinguishing,
title = "Distinguishing Between Foreground and Background Events in News",
author = "Aldawsari, Mohammed and
Perez, Adrian and
Banisakher, Deya and
Finlayson, Mark",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.453/",
doi = "10.18653/v1/2020.coling-main.453",
pages = "5171--5180"
}