@inproceedings{mendes-etal-2019-jointly,
title = "Jointly Extracting and Compressing Documents with Summary State Representations",
author = "Mendes, Afonso and
Narayan, Shashi and
Miranda, Sebasti{\~a}o and
Marinho, Zita and
Martins, Andr{\'e} F. T. and
Cohen, Shay B.",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1397",
doi = "10.18653/v1/N19-1397",
pages = "3955--3966",
abstract = "We present a new neural model for text summarization that first extracts sentences from a document and then compresses them. The pro-posed model offers a balance that sidesteps thedifficulties in abstractive methods while gener-ating more concise summaries than extractivemethods. In addition, our model dynamically determines the length of the output summary based on the gold summaries it observes during training and does not require length constraints typical to extractive summarization. The model achieves state-of-the-art results on the CNN/DailyMail and Newsroom datasets, improving over current extractive and abstractive methods. Human evaluations demonstratethat our model generates concise and informa-tive summaries. We also make available a new dataset of oracle compressive summaries derived automatically from the CNN/DailyMailreference summaries.",
}
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<abstract>We present a new neural model for text summarization that first extracts sentences from a document and then compresses them. The pro-posed model offers a balance that sidesteps thedifficulties in abstractive methods while gener-ating more concise summaries than extractivemethods. In addition, our model dynamically determines the length of the output summary based on the gold summaries it observes during training and does not require length constraints typical to extractive summarization. The model achieves state-of-the-art results on the CNN/DailyMail and Newsroom datasets, improving over current extractive and abstractive methods. Human evaluations demonstratethat our model generates concise and informa-tive summaries. We also make available a new dataset of oracle compressive summaries derived automatically from the CNN/DailyMailreference summaries.</abstract>
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%0 Conference Proceedings
%T Jointly Extracting and Compressing Documents with Summary State Representations
%A Mendes, Afonso
%A Narayan, Shashi
%A Miranda, Sebastião
%A Marinho, Zita
%A Martins, André F. T.
%A Cohen, Shay B.
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F mendes-etal-2019-jointly
%X We present a new neural model for text summarization that first extracts sentences from a document and then compresses them. The pro-posed model offers a balance that sidesteps thedifficulties in abstractive methods while gener-ating more concise summaries than extractivemethods. In addition, our model dynamically determines the length of the output summary based on the gold summaries it observes during training and does not require length constraints typical to extractive summarization. The model achieves state-of-the-art results on the CNN/DailyMail and Newsroom datasets, improving over current extractive and abstractive methods. Human evaluations demonstratethat our model generates concise and informa-tive summaries. We also make available a new dataset of oracle compressive summaries derived automatically from the CNN/DailyMailreference summaries.
%R 10.18653/v1/N19-1397
%U https://aclanthology.org/N19-1397
%U https://doi.org/10.18653/v1/N19-1397
%P 3955-3966
Markdown (Informal)
[Jointly Extracting and Compressing Documents with Summary State Representations](https://aclanthology.org/N19-1397) (Mendes et al., NAACL 2019)
ACL
- Afonso Mendes, Shashi Narayan, Sebastião Miranda, Zita Marinho, André F. T. Martins, and Shay B. Cohen. 2019. Jointly Extracting and Compressing Documents with Summary State Representations. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 3955–3966, Minneapolis, Minnesota. Association for Computational Linguistics.