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    <abstract>In this paper, we introduce Iterative Text Summarization (ITS), an iteration-based model for supervised extractive text summarization, inspired by the observation that it is often necessary for a human to read an article multiple times in order to fully understand and summarize its contents. Current summarization approaches read through a document only once to generate a document representation, resulting in a sub-optimal representation. To address this issue we introduce a model which iteratively polishes the document representation on many passes through the document. As part of our model, we also introduce a selective reading mechanism that decides more accurately the extent to which each sentence in the model should be updated. Experimental results on the CNN/DailyMail and DUC2002 datasets demonstrate that our model significantly outperforms state-of-the-art extractive systems when evaluated by machines and by humans.</abstract>
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%0 Conference Proceedings
%T Iterative Document Representation Learning Towards Summarization with Polishing
%A Chen, Xiuying
%A Gao, Shen
%A Tao, Chongyang
%A Song, Yan
%A Zhao, Dongyan
%A Yan, Rui
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F chen-etal-2018-iterative
%X In this paper, we introduce Iterative Text Summarization (ITS), an iteration-based model for supervised extractive text summarization, inspired by the observation that it is often necessary for a human to read an article multiple times in order to fully understand and summarize its contents. Current summarization approaches read through a document only once to generate a document representation, resulting in a sub-optimal representation. To address this issue we introduce a model which iteratively polishes the document representation on many passes through the document. As part of our model, we also introduce a selective reading mechanism that decides more accurately the extent to which each sentence in the model should be updated. Experimental results on the CNN/DailyMail and DUC2002 datasets demonstrate that our model significantly outperforms state-of-the-art extractive systems when evaluated by machines and by humans.
%R 10.18653/v1/D18-1442
%U https://aclanthology.org/D18-1442
%U https://doi.org/10.18653/v1/D18-1442
%P 4088-4097

Markdown (Informal)

[Iterative Document Representation Learning Towards Summarization with Polishing](https://aclanthology.org/D18-1442) (Chen et al., EMNLP 2018)

• Iterative Document Representation Learning Towards Summarization with Polishing (Chen et al., EMNLP 2018)

ACL

• Xiuying Chen, Shen Gao, Chongyang Tao, Yan Song, Dongyan Zhao, and Rui Yan. 2018. Iterative Document Representation Learning Towards Summarization with Polishing. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4088–4097, Brussels, Belgium. Association for Computational Linguistics.