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        <title>At Which Level Should We Extract? An Empirical Analysis on Extractive Document Summarization</title>
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    <abstract>Extractive methods have been proven effective in automatic document summarization. Previous works perform this task by identifying informative contents at sentence level. However, it is unclear whether performing extraction at sentence level is the best solution. In this work, we show that unnecessity and redundancy issues exist when extracting full sentences, and extracting sub-sentential units is a promising alternative. Specifically, we propose extracting sub-sentential units based on the constituency parsing tree. A neural extractive model which leverages the sub-sentential information and extracts them is presented. Extensive experiments and analyses show that extracting sub-sentential units performs competitively comparing to full sentence extraction under the evaluation of both automatic and human evaluations. Hopefully, our work could provide some inspiration of the basic extraction units in extractive summarization for future research.</abstract>
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%0 Conference Proceedings
%T At Which Level Should We Extract? An Empirical Analysis on Extractive Document Summarization
%A Zhou, Qingyu
%A Wei, Furu
%A Zhou, Ming
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F zhou-etal-2020-level
%X Extractive methods have been proven effective in automatic document summarization. Previous works perform this task by identifying informative contents at sentence level. However, it is unclear whether performing extraction at sentence level is the best solution. In this work, we show that unnecessity and redundancy issues exist when extracting full sentences, and extracting sub-sentential units is a promising alternative. Specifically, we propose extracting sub-sentential units based on the constituency parsing tree. A neural extractive model which leverages the sub-sentential information and extracts them is presented. Extensive experiments and analyses show that extracting sub-sentential units performs competitively comparing to full sentence extraction under the evaluation of both automatic and human evaluations. Hopefully, our work could provide some inspiration of the basic extraction units in extractive summarization for future research.
%R 10.18653/v1/2020.coling-main.492
%U https://aclanthology.org/2020.coling-main.492
%U https://doi.org/10.18653/v1/2020.coling-main.492
%P 5617-5628

Markdown (Informal)

[At Which Level Should We Extract? An Empirical Analysis on Extractive Document Summarization](https://aclanthology.org/2020.coling-main.492) (Zhou et al., COLING 2020)

• At Which Level Should We Extract? An Empirical Analysis on Extractive Document Summarization (Zhou et al., COLING 2020)

ACL

• Qingyu Zhou, Furu Wei, and Ming Zhou. 2020. At Which Level Should We Extract? An Empirical Analysis on Extractive Document Summarization. In Proceedings of the 28th International Conference on Computational Linguistics, pages 5617–5628, Barcelona, Spain (Online). International Committee on Computational Linguistics.