@inproceedings{field-etal-2020-generative,
title = "A Generative Approach to Titling and Clustering {W}ikipedia Sections",
author = "Field, Anjalie and
Rothe, Sascha and
Baumgartner, Simon and
Yu, Cong and
Ittycheriah, Abe",
editor = "Birch, Alexandra and
Finch, Andrew and
Hayashi, Hiroaki and
Heafield, Kenneth and
Junczys-Dowmunt, Marcin and
Konstas, Ioannis and
Li, Xian and
Neubig, Graham and
Oda, Yusuke",
booktitle = "Proceedings of the Fourth Workshop on Neural Generation and Translation",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.ngt-1.9",
doi = "10.18653/v1/2020.ngt-1.9",
pages = "79--87",
abstract = "We evaluate the performance of transformer encoders with various decoders for information organization through a new task: generation of section headings for Wikipedia articles. Our analysis shows that decoders containing attention mechanisms over the encoder output achieve high-scoring results by generating extractive text. In contrast, a decoder without attention better facilitates semantic encoding and can be used to generate section embeddings. We additionally introduce a new loss function, which further encourages the decoder to generate high-quality embeddings.",
}
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<abstract>We evaluate the performance of transformer encoders with various decoders for information organization through a new task: generation of section headings for Wikipedia articles. Our analysis shows that decoders containing attention mechanisms over the encoder output achieve high-scoring results by generating extractive text. In contrast, a decoder without attention better facilitates semantic encoding and can be used to generate section embeddings. We additionally introduce a new loss function, which further encourages the decoder to generate high-quality embeddings.</abstract>
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%0 Conference Proceedings
%T A Generative Approach to Titling and Clustering Wikipedia Sections
%A Field, Anjalie
%A Rothe, Sascha
%A Baumgartner, Simon
%A Yu, Cong
%A Ittycheriah, Abe
%Y Birch, Alexandra
%Y Finch, Andrew
%Y Hayashi, Hiroaki
%Y Heafield, Kenneth
%Y Junczys-Dowmunt, Marcin
%Y Konstas, Ioannis
%Y Li, Xian
%Y Neubig, Graham
%Y Oda, Yusuke
%S Proceedings of the Fourth Workshop on Neural Generation and Translation
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F field-etal-2020-generative
%X We evaluate the performance of transformer encoders with various decoders for information organization through a new task: generation of section headings for Wikipedia articles. Our analysis shows that decoders containing attention mechanisms over the encoder output achieve high-scoring results by generating extractive text. In contrast, a decoder without attention better facilitates semantic encoding and can be used to generate section embeddings. We additionally introduce a new loss function, which further encourages the decoder to generate high-quality embeddings.
%R 10.18653/v1/2020.ngt-1.9
%U https://aclanthology.org/2020.ngt-1.9
%U https://doi.org/10.18653/v1/2020.ngt-1.9
%P 79-87
Markdown (Informal)
[A Generative Approach to Titling and Clustering Wikipedia Sections](https://aclanthology.org/2020.ngt-1.9) (Field et al., NGT 2020)
ACL