Abstract
In a text, entities mentioned earlier can be referred to in later discourse by a more general description. For example, Celine Dion and Justin Bieber can be referred to by Canadian singers or celebrities. In this work, we study this phenomenon in the context of summarization, where entities from a source text are generalized in the summary. We call such instances source-summary entity aggregations. We categorize these aggregations into two types and analyze them in the Cnn/Dailymail corpus, showing that they are reasonably frequent. We then examine how well three state-of-the-art summarization systems can generate such aggregations within summaries. We also develop techniques to encourage them to generate more aggregations. Our results show that there is significant room for improvement in producing semantically correct aggregations.- Anthology ID:
- 2022.coling-1.526
- 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:
- 6019–6034
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.526
- DOI:
- Bibkey:
- Cite (ACL):
- José Ángel González, Annie Louis, and Jackie Chi Kit Cheung. 2022. Source-summary Entity Aggregation in Abstractive Summarization. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6019–6034, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Source-summary Entity Aggregation in Abstractive Summarization (González et al., COLING 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.526.pdf
Export citation
@inproceedings{gonzalez-etal-2022-source,
title = "Source-summary Entity Aggregation in Abstractive Summarization",
author = "Gonz{\'a}lez, Jos{\'e} {\'A}ngel and
Louis, Annie and
Cheung, Jackie Chi Kit",
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-1.526",
pages = "6019--6034",
abstract = "In a text, entities mentioned earlier can be referred to in later discourse by a more general description. For example, \textit{Celine Dion} and \textit{Justin Bieber} can be referred to by \textit{Canadian singers} or \textit{celebrities}. In this work, we study this phenomenon in the context of summarization, where entities from a source text are generalized in the summary. We call such instances \textit{source-summary entity aggregations}. We categorize these aggregations into two types and analyze them in the Cnn/Dailymail corpus, showing that they are reasonably frequent. We then examine how well three state-of-the-art summarization systems can generate such aggregations within summaries. We also develop techniques to encourage them to generate more aggregations. Our results show that there is significant room for improvement in producing semantically correct aggregations.",
}
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<abstract>In a text, entities mentioned earlier can be referred to in later discourse by a more general description. For example, Celine Dion and Justin Bieber can be referred to by Canadian singers or celebrities. In this work, we study this phenomenon in the context of summarization, where entities from a source text are generalized in the summary. We call such instances source-summary entity aggregations. We categorize these aggregations into two types and analyze them in the Cnn/Dailymail corpus, showing that they are reasonably frequent. We then examine how well three state-of-the-art summarization systems can generate such aggregations within summaries. We also develop techniques to encourage them to generate more aggregations. Our results show that there is significant room for improvement in producing semantically correct aggregations.</abstract>
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%0 Conference Proceedings %T Source-summary Entity Aggregation in Abstractive Summarization %A González, José Ángel %A Louis, Annie %A Cheung, Jackie Chi Kit %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 gonzalez-etal-2022-source %X In a text, entities mentioned earlier can be referred to in later discourse by a more general description. For example, Celine Dion and Justin Bieber can be referred to by Canadian singers or celebrities. In this work, we study this phenomenon in the context of summarization, where entities from a source text are generalized in the summary. We call such instances source-summary entity aggregations. We categorize these aggregations into two types and analyze them in the Cnn/Dailymail corpus, showing that they are reasonably frequent. We then examine how well three state-of-the-art summarization systems can generate such aggregations within summaries. We also develop techniques to encourage them to generate more aggregations. Our results show that there is significant room for improvement in producing semantically correct aggregations. %U https://aclanthology.org/2022.coling-1.526 %P 6019-6034
Markdown (Informal)
[Source-summary Entity Aggregation in Abstractive Summarization](https://aclanthology.org/2022.coling-1.526) (González et al., COLING 2022)
- Source-summary Entity Aggregation in Abstractive Summarization (González et al., COLING 2022)
ACL
- José Ángel González, Annie Louis, and Jackie Chi Kit Cheung. 2022. Source-summary Entity Aggregation in Abstractive Summarization. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6019–6034, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.