@inproceedings{fan-etal-2018-controllable,
title = "Controllable Abstractive Summarization",
author = "Fan, Angela and
Grangier, David and
Auli, Michael",
editor = "Birch, Alexandra and
Finch, Andrew and
Luong, Thang and
Neubig, Graham and
Oda, Yusuke",
booktitle = "Proceedings of the 2nd Workshop on Neural Machine Translation and Generation",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-2706",
doi = "10.18653/v1/W18-2706",
pages = "45--54",
abstract = "Current models for document summarization disregard user preferences such as the desired length, style, the entities that the user might be interested in, or how much of the document the user has already read. We present a neural summarization model with a simple but effective mechanism to enable users to specify these high level attributes in order to control the shape of the final summaries to better suit their needs. With user input, our system can produce high quality summaries that follow user preferences. Without user input, we set the control variables automatically {--} on the full text CNN-Dailymail dataset, we outperform state of the art abstractive systems (both in terms of F1-ROUGE1 40.38 vs. 39.53 F1-ROUGE and human evaluation.",
}
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<abstract>Current models for document summarization disregard user preferences such as the desired length, style, the entities that the user might be interested in, or how much of the document the user has already read. We present a neural summarization model with a simple but effective mechanism to enable users to specify these high level attributes in order to control the shape of the final summaries to better suit their needs. With user input, our system can produce high quality summaries that follow user preferences. Without user input, we set the control variables automatically – on the full text CNN-Dailymail dataset, we outperform state of the art abstractive systems (both in terms of F1-ROUGE1 40.38 vs. 39.53 F1-ROUGE and human evaluation.</abstract>
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%0 Conference Proceedings
%T Controllable Abstractive Summarization
%A Fan, Angela
%A Grangier, David
%A Auli, Michael
%Y Birch, Alexandra
%Y Finch, Andrew
%Y Luong, Thang
%Y Neubig, Graham
%Y Oda, Yusuke
%S Proceedings of the 2nd Workshop on Neural Machine Translation and Generation
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F fan-etal-2018-controllable
%X Current models for document summarization disregard user preferences such as the desired length, style, the entities that the user might be interested in, or how much of the document the user has already read. We present a neural summarization model with a simple but effective mechanism to enable users to specify these high level attributes in order to control the shape of the final summaries to better suit their needs. With user input, our system can produce high quality summaries that follow user preferences. Without user input, we set the control variables automatically – on the full text CNN-Dailymail dataset, we outperform state of the art abstractive systems (both in terms of F1-ROUGE1 40.38 vs. 39.53 F1-ROUGE and human evaluation.
%R 10.18653/v1/W18-2706
%U https://aclanthology.org/W18-2706
%U https://doi.org/10.18653/v1/W18-2706
%P 45-54
Markdown (Informal)
[Controllable Abstractive Summarization](https://aclanthology.org/W18-2706) (Fan et al., NGT 2018)
ACL
- Angela Fan, David Grangier, and Michael Auli. 2018. Controllable Abstractive Summarization. In Proceedings of the 2nd Workshop on Neural Machine Translation and Generation, pages 45–54, Melbourne, Australia. Association for Computational Linguistics.