@inproceedings{liu-etal-2018-automatic,
title = "Automatic Event Salience Identification",
author = "Liu, Zhengzhong and
Xiong, Chenyan and
Mitamura, Teruko and
Hovy, Eduard",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1154",
doi = "10.18653/v1/D18-1154",
pages = "1226--1236",
abstract = "Identifying the salience (i.e. importance) of discourse units is an important task in language understanding. While events play important roles in text documents, little research exists on analyzing their saliency status. This paper empirically studies Event Salience and proposes two salience detection models based on discourse relations. The first is a feature based salience model that incorporates cohesion among discourse units. The second is a neural model that captures more complex interactions between discourse units. In our new large-scale event salience corpus, both methods significantly outperform the strong frequency baseline, while our neural model further improves the feature based one by a large margin. Our analyses demonstrate that our neural model captures interesting connections between salience and discourse unit relations (e.g., scripts and frame structures).",
}
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<abstract>Identifying the salience (i.e. importance) of discourse units is an important task in language understanding. While events play important roles in text documents, little research exists on analyzing their saliency status. This paper empirically studies Event Salience and proposes two salience detection models based on discourse relations. The first is a feature based salience model that incorporates cohesion among discourse units. The second is a neural model that captures more complex interactions between discourse units. In our new large-scale event salience corpus, both methods significantly outperform the strong frequency baseline, while our neural model further improves the feature based one by a large margin. Our analyses demonstrate that our neural model captures interesting connections between salience and discourse unit relations (e.g., scripts and frame structures).</abstract>
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%0 Conference Proceedings
%T Automatic Event Salience Identification
%A Liu, Zhengzhong
%A Xiong, Chenyan
%A Mitamura, Teruko
%A Hovy, Eduard
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F liu-etal-2018-automatic
%X Identifying the salience (i.e. importance) of discourse units is an important task in language understanding. While events play important roles in text documents, little research exists on analyzing their saliency status. This paper empirically studies Event Salience and proposes two salience detection models based on discourse relations. The first is a feature based salience model that incorporates cohesion among discourse units. The second is a neural model that captures more complex interactions between discourse units. In our new large-scale event salience corpus, both methods significantly outperform the strong frequency baseline, while our neural model further improves the feature based one by a large margin. Our analyses demonstrate that our neural model captures interesting connections between salience and discourse unit relations (e.g., scripts and frame structures).
%R 10.18653/v1/D18-1154
%U https://aclanthology.org/D18-1154
%U https://doi.org/10.18653/v1/D18-1154
%P 1226-1236
Markdown (Informal)
[Automatic Event Salience Identification](https://aclanthology.org/D18-1154) (Liu et al., EMNLP 2018)
ACL
- Zhengzhong Liu, Chenyan Xiong, Teruko Mitamura, and Eduard Hovy. 2018. Automatic Event Salience Identification. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1226–1236, Brussels, Belgium. Association for Computational Linguistics.