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        <title>Exploiting Discourse-Level Segmentation for Extractive Summarization</title>
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        <namePart type="given">Zhengyuan</namePart>
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            <namePart type="given">Lu</namePart>
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    <abstract>Extractive summarization selects and concatenates the most essential text spans in a document. Most, if not all, neural approaches use sentences as the elementary unit to select content for summarization. However, semantic segments containing supplementary information or descriptive details are often nonessential in the generated summaries. In this work, we propose to exploit discourse-level segmentation as a finer-grained means to more precisely pinpoint the core content in a document. We investigate how the sub-sentential segmentation improves extractive summarization performance when content selection is modeled through two basic neural network architectures and a deep bi-directional transformer. Experiment results on the CNN/Daily Mail dataset show that discourse-level segmentation is effective in both cases. In particular, we achieve state-of-the-art performance when discourse-level segmentation is combined with our adapted contextual representation model.</abstract>
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    <identifier type="doi">10.18653/v1/D19-5415</identifier>
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%0 Conference Proceedings
%T Exploiting Discourse-Level Segmentation for Extractive Summarization
%A Liu, Zhengyuan
%A Chen, Nancy
%Y Wang, Lu
%Y Cheung, Jackie Chi Kit
%Y Carenini, Giuseppe
%Y Liu, Fei
%S Proceedings of the 2nd Workshop on New Frontiers in Summarization
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F liu-chen-2019-exploiting
%X Extractive summarization selects and concatenates the most essential text spans in a document. Most, if not all, neural approaches use sentences as the elementary unit to select content for summarization. However, semantic segments containing supplementary information or descriptive details are often nonessential in the generated summaries. In this work, we propose to exploit discourse-level segmentation as a finer-grained means to more precisely pinpoint the core content in a document. We investigate how the sub-sentential segmentation improves extractive summarization performance when content selection is modeled through two basic neural network architectures and a deep bi-directional transformer. Experiment results on the CNN/Daily Mail dataset show that discourse-level segmentation is effective in both cases. In particular, we achieve state-of-the-art performance when discourse-level segmentation is combined with our adapted contextual representation model.
%R 10.18653/v1/D19-5415
%U https://aclanthology.org/D19-5415
%U https://doi.org/10.18653/v1/D19-5415
%P 116-121

Markdown (Informal)

[Exploiting Discourse-Level Segmentation for Extractive Summarization](https://aclanthology.org/D19-5415) (Liu & Chen, 2019)

• Exploiting Discourse-Level Segmentation for Extractive Summarization (Liu & Chen, 2019)

ACL

• Zhengyuan Liu and Nancy Chen. 2019. Exploiting Discourse-Level Segmentation for Extractive Summarization. In Proceedings of the 2nd Workshop on New Frontiers in Summarization, pages 116–121, Hong Kong, China. Association for Computational Linguistics.