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        <title>Towards Annotating and Creating Summary Highlights at Sub-sentence Level</title>
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        <namePart type="given">Kristjan</namePart>
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            <title>Proceedings of the 2nd Workshop on New Frontiers in Summarization</title>
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            <namePart type="given">Lu</namePart>
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            <namePart type="given">Jackie</namePart>
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    <abstract>Highlighting is a powerful tool to pick out important content and emphasize. Creating summary highlights at the sub-sentence level is particularly desirable, because sub-sentences are more concise than whole sentences. They are also better suited than individual words and phrases that can potentially lead to disfluent, fragmented summaries. In this paper we seek to generate summary highlights by annotating summary-worthy sub-sentences and teaching classifiers to do the same. We frame the task as jointly selecting important sentences and identifying a single most informative textual unit from each sentence. This formulation dramatically reduces the task complexity involved in sentence compression. Our study provides new benchmarks and baselines for generating highlights at the sub-sentence level.</abstract>
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    <identifier type="doi">10.18653/v1/D19-5408</identifier>
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        <date>2019-11</date>
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%0 Conference Proceedings
%T Towards Annotating and Creating Summary Highlights at Sub-sentence Level
%A Arumae, Kristjan
%A Bhatia, Parminder
%A Liu, Fei
%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 arumae-etal-2019-towards
%X Highlighting is a powerful tool to pick out important content and emphasize. Creating summary highlights at the sub-sentence level is particularly desirable, because sub-sentences are more concise than whole sentences. They are also better suited than individual words and phrases that can potentially lead to disfluent, fragmented summaries. In this paper we seek to generate summary highlights by annotating summary-worthy sub-sentences and teaching classifiers to do the same. We frame the task as jointly selecting important sentences and identifying a single most informative textual unit from each sentence. This formulation dramatically reduces the task complexity involved in sentence compression. Our study provides new benchmarks and baselines for generating highlights at the sub-sentence level.
%R 10.18653/v1/D19-5408
%U https://aclanthology.org/D19-5408
%U https://doi.org/10.18653/v1/D19-5408
%P 64-69

Markdown (Informal)

[Towards Annotating and Creating Summary Highlights at Sub-sentence Level](https://aclanthology.org/D19-5408) (Arumae et al., 2019)

• Towards Annotating and Creating Summary Highlights at Sub-sentence Level (Arumae et al., 2019)

ACL

• Kristjan Arumae, Parminder Bhatia, and Fei Liu. 2019. Towards Annotating and Creating Summary Highlights at Sub-sentence Level. In Proceedings of the 2nd Workshop on New Frontiers in Summarization, pages 64–69, Hong Kong, China. Association for Computational Linguistics.