Correct Metadata for
Abstract
Summaries, keyphrases, and titles are different ways of concisely capturing the content of a document. While most previous work has released the datasets of keyphrases and summarization separately, in this work, we introduce LipKey, the largest news corpus with human-written abstractive summaries, absent keyphrases, and titles. We jointly use the three elements via multi-task training and training as joint structured inputs, in the context of document summarization. We find that including absent keyphrases and titles as additional context to the source document improves transformer-based summarization models.- Anthology ID:
- 2022.coling-1.303
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 3427–3437
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.303/
- DOI:
- Bibkey:
- Cite (ACL):
- Fajri Koto, Timothy Baldwin, and Jey Han Lau. 2022. LipKey: A Large-Scale News Dataset for Absent Keyphrases Generation and Abstractive Summarization. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3427–3437, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- LipKey: A Large-Scale News Dataset for Absent Keyphrases Generation and Abstractive Summarization (Koto et al., COLING 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.303.pdf
- Data
- IndoSum, KPTimes, Liputan6
Export citation
@inproceedings{koto-etal-2022-lipkey,
title = "{L}ip{K}ey: A Large-Scale News Dataset for Absent Keyphrases Generation and Abstractive Summarization",
author = "Koto, Fajri and
Baldwin, Timothy and
Lau, Jey Han",
editor = "Calzolari, Nicoletta and
Huang, Chu-Ren and
Kim, Hansaem and
Pustejovsky, James and
Wanner, Leo and
Choi, Key-Sun and
Ryu, Pum-Mo and
Chen, Hsin-Hsi and
Donatelli, Lucia and
Ji, Heng and
Kurohashi, Sadao and
Paggio, Patrizia and
Xue, Nianwen and
Kim, Seokhwan and
Hahm, Younggyun and
He, Zhong and
Lee, Tony Kyungil and
Santus, Enrico and
Bond, Francis and
Na, Seung-Hoon",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.303/",
pages = "3427--3437",
abstract = "Summaries, keyphrases, and titles are different ways of concisely capturing the content of a document. While most previous work has released the datasets of keyphrases and summarization separately, in this work, we introduce LipKey, the largest news corpus with human-written abstractive summaries, absent keyphrases, and titles. We jointly use the three elements via multi-task training and training as joint structured inputs, in the context of document summarization. We find that including absent keyphrases and titles as additional context to the source document improves transformer-based summarization models."
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%0 Conference Proceedings %T LipKey: A Large-Scale News Dataset for Absent Keyphrases Generation and Abstractive Summarization %A Koto, Fajri %A Baldwin, Timothy %A Lau, Jey Han %Y Calzolari, Nicoletta %Y Huang, Chu-Ren %Y Kim, Hansaem %Y Pustejovsky, James %Y Wanner, Leo %Y Choi, Key-Sun %Y Ryu, Pum-Mo %Y Chen, Hsin-Hsi %Y Donatelli, Lucia %Y Ji, Heng %Y Kurohashi, Sadao %Y Paggio, Patrizia %Y Xue, Nianwen %Y Kim, Seokhwan %Y Hahm, Younggyun %Y He, Zhong %Y Lee, Tony Kyungil %Y Santus, Enrico %Y Bond, Francis %Y Na, Seung-Hoon %S Proceedings of the 29th International Conference on Computational Linguistics %D 2022 %8 October %I International Committee on Computational Linguistics %C Gyeongju, Republic of Korea %F koto-etal-2022-lipkey %X Summaries, keyphrases, and titles are different ways of concisely capturing the content of a document. While most previous work has released the datasets of keyphrases and summarization separately, in this work, we introduce LipKey, the largest news corpus with human-written abstractive summaries, absent keyphrases, and titles. We jointly use the three elements via multi-task training and training as joint structured inputs, in the context of document summarization. We find that including absent keyphrases and titles as additional context to the source document improves transformer-based summarization models. %U https://aclanthology.org/2022.coling-1.303/ %P 3427-3437
Markdown (Informal)
[LipKey: A Large-Scale News Dataset for Absent Keyphrases Generation and Abstractive Summarization](https://aclanthology.org/2022.coling-1.303/) (Koto et al., COLING 2022)
- LipKey: A Large-Scale News Dataset for Absent Keyphrases Generation and Abstractive Summarization (Koto et al., COLING 2022)
ACL
- Fajri Koto, Timothy Baldwin, and Jey Han Lau. 2022. LipKey: A Large-Scale News Dataset for Absent Keyphrases Generation and Abstractive Summarization. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3427–3437, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.