@inproceedings{mrini-etal-2021-rewards,
title = "Rewards with Negative Examples for Reinforced Topic-Focused Abstractive Summarization",
author = "Mrini, Khalil and
Liu, Can and
Dreyer, Markus",
editor = "Carenini, Giuseppe and
Cheung, Jackie Chi Kit and
Dong, Yue and
Liu, Fei and
Wang, Lu",
booktitle = "Proceedings of the Third Workshop on New Frontiers in Summarization",
month = nov,
year = "2021",
address = "Online and in Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.newsum-1.4",
doi = "10.18653/v1/2021.newsum-1.4",
pages = "33--38",
abstract = "We consider the problem of topic-focused abstractive summarization, where the goal is to generate an abstractive summary focused on a particular topic, a phrase of one or multiple words. We hypothesize that the task of generating topic-focused summaries can be improved by showing the model what it must not focus on. We introduce a deep reinforcement learning approach to topic-focused abstractive summarization, trained on rewards with a novel negative example baseline. We define the input in this problem as the source text preceded by the topic. We adapt the CNN-Daily Mail and New York Times summarization datasets for this task. We then show through experiments on existing rewards that the use of a negative example baseline can outperform the use of a self-critical baseline, in Rouge, BERTScore, and human evaluation metrics.",
}
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<abstract>We consider the problem of topic-focused abstractive summarization, where the goal is to generate an abstractive summary focused on a particular topic, a phrase of one or multiple words. We hypothesize that the task of generating topic-focused summaries can be improved by showing the model what it must not focus on. We introduce a deep reinforcement learning approach to topic-focused abstractive summarization, trained on rewards with a novel negative example baseline. We define the input in this problem as the source text preceded by the topic. We adapt the CNN-Daily Mail and New York Times summarization datasets for this task. We then show through experiments on existing rewards that the use of a negative example baseline can outperform the use of a self-critical baseline, in Rouge, BERTScore, and human evaluation metrics.</abstract>
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%0 Conference Proceedings
%T Rewards with Negative Examples for Reinforced Topic-Focused Abstractive Summarization
%A Mrini, Khalil
%A Liu, Can
%A Dreyer, Markus
%Y Carenini, Giuseppe
%Y Cheung, Jackie Chi Kit
%Y Dong, Yue
%Y Liu, Fei
%Y Wang, Lu
%S Proceedings of the Third Workshop on New Frontiers in Summarization
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and in Dominican Republic
%F mrini-etal-2021-rewards
%X We consider the problem of topic-focused abstractive summarization, where the goal is to generate an abstractive summary focused on a particular topic, a phrase of one or multiple words. We hypothesize that the task of generating topic-focused summaries can be improved by showing the model what it must not focus on. We introduce a deep reinforcement learning approach to topic-focused abstractive summarization, trained on rewards with a novel negative example baseline. We define the input in this problem as the source text preceded by the topic. We adapt the CNN-Daily Mail and New York Times summarization datasets for this task. We then show through experiments on existing rewards that the use of a negative example baseline can outperform the use of a self-critical baseline, in Rouge, BERTScore, and human evaluation metrics.
%R 10.18653/v1/2021.newsum-1.4
%U https://aclanthology.org/2021.newsum-1.4
%U https://doi.org/10.18653/v1/2021.newsum-1.4
%P 33-38
Markdown (Informal)
[Rewards with Negative Examples for Reinforced Topic-Focused Abstractive Summarization](https://aclanthology.org/2021.newsum-1.4) (Mrini et al., NewSum 2021)
ACL