Constrained Regeneration for Cross-Lingual Query-Focused Extractive Summarization
Elsbeth Turcan, David Wan, Faisal Ladhak, Petra Galuscakova, Sukanta Sen, Svetlana Tchistiakova, Weijia Xu, Marine Carpuat, Kenneth Heafield, Douglas Oard, Kathleen McKeown
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Abstract
Query-focused summaries of foreign-language, retrieved documents can help a user understand whether a document is actually relevant to the query term. A standard approach to this problem is to first translate the source documents and then perform extractive summarization to find relevant snippets. However, in a cross-lingual setting, the query term does not necessarily appear in the translations of relevant documents. In this work, we show that constrained machine translation and constrained post-editing can improve human relevance judgments by including a query term in a summary when its translation appears in the source document. We also present several strategies for selecting only certain documents for regeneration which yield further improvements- Anthology ID:
- 2022.coling-1.236
- 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:
- 2668–2680
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.236/
- DOI:
- Bibkey:
- Cite (ACL):
- Elsbeth Turcan, David Wan, Faisal Ladhak, Petra Galuscakova, Sukanta Sen, Svetlana Tchistiakova, Weijia Xu, Marine Carpuat, Kenneth Heafield, Douglas Oard, and Kathleen McKeown. 2022. Constrained Regeneration for Cross-Lingual Query-Focused Extractive Summarization. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2668–2680, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Constrained Regeneration for Cross-Lingual Query-Focused Extractive Summarization (Turcan et al., COLING 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.236.pdf
Export citation
@inproceedings{turcan-etal-2022-constrained, title = "Constrained Regeneration for Cross-Lingual Query-Focused Extractive Summarization", author = "Turcan, Elsbeth and Wan, David and Ladhak, Faisal and Galuscakova, Petra and Sen, Sukanta and Tchistiakova, Svetlana and Xu, Weijia and Carpuat, Marine and Heafield, Kenneth and Oard, Douglas and McKeown, Kathleen", 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-1.236/", pages = "2668--2680", abstract = "Query-focused summaries of foreign-language, retrieved documents can help a user understand whether a document is actually relevant to the query term. A standard approach to this problem is to first translate the source documents and then perform extractive summarization to find relevant snippets. However, in a cross-lingual setting, the query term does not necessarily appear in the translations of relevant documents. In this work, we show that constrained machine translation and constrained post-editing can improve human relevance judgments by including a query term in a summary when its translation appears in the source document. We also present several strategies for selecting only certain documents for regeneration which yield further improvements" }
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%0 Conference Proceedings %T Constrained Regeneration for Cross-Lingual Query-Focused Extractive Summarization %A Turcan, Elsbeth %A Wan, David %A Ladhak, Faisal %A Galuscakova, Petra %A Sen, Sukanta %A Tchistiakova, Svetlana %A Xu, Weijia %A Carpuat, Marine %A Heafield, Kenneth %A Oard, Douglas %A McKeown, Kathleen %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 turcan-etal-2022-constrained %X Query-focused summaries of foreign-language, retrieved documents can help a user understand whether a document is actually relevant to the query term. A standard approach to this problem is to first translate the source documents and then perform extractive summarization to find relevant snippets. However, in a cross-lingual setting, the query term does not necessarily appear in the translations of relevant documents. In this work, we show that constrained machine translation and constrained post-editing can improve human relevance judgments by including a query term in a summary when its translation appears in the source document. We also present several strategies for selecting only certain documents for regeneration which yield further improvements %U https://aclanthology.org/2022.coling-1.236/ %P 2668-2680
Markdown (Informal)
[Constrained Regeneration for Cross-Lingual Query-Focused Extractive Summarization](https://aclanthology.org/2022.coling-1.236/) (Turcan et al., COLING 2022)
- Constrained Regeneration for Cross-Lingual Query-Focused Extractive Summarization (Turcan et al., COLING 2022)
ACL
- Elsbeth Turcan, David Wan, Faisal Ladhak, Petra Galuscakova, Sukanta Sen, Svetlana Tchistiakova, Weijia Xu, Marine Carpuat, Kenneth Heafield, Douglas Oard, and Kathleen McKeown. 2022. Constrained Regeneration for Cross-Lingual Query-Focused Extractive Summarization. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2668–2680, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.