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        <title>Coarse-to-Fine Attention Models for Document Summarization</title>
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            <title>Proceedings of the Workshop on New Frontiers in Summarization</title>
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            <namePart type="given">Lu</namePart>
            <namePart type="family">Wang</namePart>
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    <abstract>Sequence-to-sequence models with attention have been successful for a variety of NLP problems, but their speed does not scale well for tasks with long source sequences such as document summarization. We propose a novel coarse-to-fine attention model that hierarchically reads a document, using coarse attention to select top-level chunks of text and fine attention to read the words of the chosen chunks. While the computation for training standard attention models scales linearly with source sequence length, our method scales with the number of top-level chunks and can handle much longer sequences. Empirically, we find that while coarse-to-fine attention models lag behind state-of-the-art baselines, our method achieves the desired behavior of sparsely attending to subsets of the document for generation.</abstract>
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%0 Conference Proceedings
%T Coarse-to-Fine Attention Models for Document Summarization
%A Ling, Jeffrey
%A Rush, Alexander
%Y Wang, Lu
%Y Cheung, Jackie Chi Kit
%Y Carenini, Giuseppe
%Y Liu, Fei
%S Proceedings of the Workshop on New Frontiers in Summarization
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F ling-rush-2017-coarse
%X Sequence-to-sequence models with attention have been successful for a variety of NLP problems, but their speed does not scale well for tasks with long source sequences such as document summarization. We propose a novel coarse-to-fine attention model that hierarchically reads a document, using coarse attention to select top-level chunks of text and fine attention to read the words of the chosen chunks. While the computation for training standard attention models scales linearly with source sequence length, our method scales with the number of top-level chunks and can handle much longer sequences. Empirically, we find that while coarse-to-fine attention models lag behind state-of-the-art baselines, our method achieves the desired behavior of sparsely attending to subsets of the document for generation.
%R 10.18653/v1/W17-4505
%U https://aclanthology.org/W17-4505
%U https://doi.org/10.18653/v1/W17-4505
%P 33-42

Markdown (Informal)

[Coarse-to-Fine Attention Models for Document Summarization](https://aclanthology.org/W17-4505) (Ling & Rush, 2017)

• Coarse-to-Fine Attention Models for Document Summarization (Ling & Rush, 2017)

ACL

• Jeffrey Ling and Alexander Rush. 2017. Coarse-to-Fine Attention Models for Document Summarization. In Proceedings of the Workshop on New Frontiers in Summarization, pages 33–42, Copenhagen, Denmark. Association for Computational Linguistics.