We examine the benefit of a variety of discourse and semantic features for the identification of summary-worthy content in narrative stories. Using logistic regression models, we find that the most informative features are those that relate to the narrative structure of a text. We show that automatic methods for feature extraction perform significantly worse than full manual annotation, but that with optimization, a fully automatic approach can outperform a variety of existing extractive approaches to summarization.
|Titel||Proceedings of the 13th IEEE International Conference on Semantic Computing (ICSC)|
|ISBN van geprinte versie||978-1-5386-6783-5|
|Status||Published - 2019|
|Evenement||13th IEEE International Conference on Semantic Computing (ICSC) - Newport Beach Mariott Bayview, Newport Beach, United States|
Duur: 30 jan 2019 → 1 feb 2019
|Conference||13th IEEE International Conference on Semantic Computing (ICSC)|
|Periode||30/01/19 → 1/02/19|