@inproceedings{nair-singh-2021-improving-abstractive,
title = "Improving Abstractive Summarization with Commonsense Knowledge",
author = "Nair, Pranav and
Singh, Anil Kumar",
editor = "Djabri, Souhila and
Gimadi, Dinara and
Mihaylova, Tsvetomila and
Nikolova-Koleva, Ivelina",
booktitle = "Proceedings of the Student Research Workshop Associated with RANLP 2021",
month = sep,
year = "2021",
address = "Online",
publisher = "INCOMA Ltd.",
url = "https://aclanthology.org/2021.ranlp-srw.19",
pages = "135--143",
abstract = "Large scale pretrained models have demonstrated strong performances on several natural language generation and understanding benchmarks. However, introducing commonsense into them to generate more realistic text remains a challenge. Inspired from previous work on commonsense knowledge generation and generative commonsense reasoning, we introduce two methods to add commonsense reasoning skills and knowledge into abstractive summarization models. Both methods beat the baseline on ROUGE scores, demonstrating the superiority of our models over the baseline. Human evaluation results suggest that summaries generated by our methods are more realistic and have fewer commonsensical errors.",
}
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%0 Conference Proceedings
%T Improving Abstractive Summarization with Commonsense Knowledge
%A Nair, Pranav
%A Singh, Anil Kumar
%Y Djabri, Souhila
%Y Gimadi, Dinara
%Y Mihaylova, Tsvetomila
%Y Nikolova-Koleva, Ivelina
%S Proceedings of the Student Research Workshop Associated with RANLP 2021
%D 2021
%8 September
%I INCOMA Ltd.
%C Online
%F nair-singh-2021-improving-abstractive
%X Large scale pretrained models have demonstrated strong performances on several natural language generation and understanding benchmarks. However, introducing commonsense into them to generate more realistic text remains a challenge. Inspired from previous work on commonsense knowledge generation and generative commonsense reasoning, we introduce two methods to add commonsense reasoning skills and knowledge into abstractive summarization models. Both methods beat the baseline on ROUGE scores, demonstrating the superiority of our models over the baseline. Human evaluation results suggest that summaries generated by our methods are more realistic and have fewer commonsensical errors.
%U https://aclanthology.org/2021.ranlp-srw.19
%P 135-143
Markdown (Informal)
[Improving Abstractive Summarization with Commonsense Knowledge](https://aclanthology.org/2021.ranlp-srw.19) (Nair & Singh, RANLP 2021)
ACL