@inproceedings{hua-wang-2017-pilot,
title = "A Pilot Study of Domain Adaptation Effect for Neural Abstractive Summarization",
author = "Hua, Xinyu and
Wang, Lu",
editor = "Wang, Lu and
Cheung, Jackie Chi Kit and
Carenini, Giuseppe and
Liu, Fei",
booktitle = "Proceedings of the Workshop on New Frontiers in Summarization",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-4513",
doi = "10.18653/v1/W17-4513",
pages = "100--106",
abstract = "We study the problem of domain adaptation for neural abstractive summarization. We make initial efforts in investigating what information can be transferred to a new domain. Experimental results on news stories and opinion articles indicate that neural summarization model benefits from pre-training based on extractive summaries. We also find that the combination of in-domain and out-of-domain setup yields better summaries when in-domain data is insufficient. Further analysis shows that, the model is capable to select salient content even trained on out-of-domain data, but requires in-domain data to capture the style for a target domain.",
}
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%0 Conference Proceedings
%T A Pilot Study of Domain Adaptation Effect for Neural Abstractive Summarization
%A Hua, Xinyu
%A Wang, Lu
%Y Wang, Lu
%Y Cheung, Jackie Chi Kit
%Y Carenini, Giuseppe
%Y Liu, Fei
%S Proceedings of the Workshop on New Frontiers in Summarization
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F hua-wang-2017-pilot
%X We study the problem of domain adaptation for neural abstractive summarization. We make initial efforts in investigating what information can be transferred to a new domain. Experimental results on news stories and opinion articles indicate that neural summarization model benefits from pre-training based on extractive summaries. We also find that the combination of in-domain and out-of-domain setup yields better summaries when in-domain data is insufficient. Further analysis shows that, the model is capable to select salient content even trained on out-of-domain data, but requires in-domain data to capture the style for a target domain.
%R 10.18653/v1/W17-4513
%U https://aclanthology.org/W17-4513
%U https://doi.org/10.18653/v1/W17-4513
%P 100-106
Markdown (Informal)
[A Pilot Study of Domain Adaptation Effect for Neural Abstractive Summarization](https://aclanthology.org/W17-4513) (Hua & Wang, 2017)
ACL