@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",
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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<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</abstract>
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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
%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)
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.