Paper Info
Title
Constrained Regeneration for Cross-Lingual Query-Focused Extractive Summarization.
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
Year
Venue
DocType
2022
International Conference on Computational Linguistics
Conference
Volume
Citations 
PageRank 
Proceedings of the 29th International Conference on Computational Linguistics
0
0.34
References 
Authors
0
11
Name
Order
Citations
PageRank
Elsbeth Turcan100.34
David Wan201.35
Faisal Ladhak352.76
Petra Galuscakova400.34
Sukanta Sen500.68
Svetlana Tchistiakova601.35
Weijia Xu705.75
Marine Carpuat8133.31
Kenneth Heafield957939.46
Douglas W. Oard102484246.11
Kathleen R. McKeown114990741.29