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    <abstract>In this paper, we propose a novel neural single-document extractive summarization model for long documents, incorporating both the global context of the whole document and the local context within the current topic. We evaluate the model on two datasets of scientific papers , Pubmed and arXiv, where it outperforms previous work, both extractive and abstractive models, on ROUGE-1, ROUGE-2 and METEOR scores. We also show that, consistently with our goal, the benefits of our method become stronger as we apply it to longer documents. Rather surprisingly, an ablation study indicates that the benefits of our model seem to come exclusively from modeling the local context, even for the longest documents.</abstract>
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    <identifier type="doi">10.18653/v1/D19-1298</identifier>
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%0 Conference Proceedings
%T Extractive Summarization of Long Documents by Combining Global and Local Context
%A Xiao, Wen
%A Carenini, Giuseppe
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 nov
%I Association for Computational Linguistics
%C Hong Kong, China
%F xiao-carenini-2019-extractive
%X In this paper, we propose a novel neural single-document extractive summarization model for long documents, incorporating both the global context of the whole document and the local context within the current topic. We evaluate the model on two datasets of scientific papers , Pubmed and arXiv, where it outperforms previous work, both extractive and abstractive models, on ROUGE-1, ROUGE-2 and METEOR scores. We also show that, consistently with our goal, the benefits of our method become stronger as we apply it to longer documents. Rather surprisingly, an ablation study indicates that the benefits of our model seem to come exclusively from modeling the local context, even for the longest documents.
%R 10.18653/v1/D19-1298
%U https://aclanthology.org/D19-1298
%U https://doi.org/10.18653/v1/D19-1298
%P 3011-3021

Markdown (Informal)

[Extractive Summarization of Long Documents by Combining Global and Local Context](https://aclanthology.org/D19-1298) (Xiao & Carenini, EMNLP 2019)

• Extractive Summarization of Long Documents by Combining Global and Local Context (Xiao & Carenini, EMNLP 2019)

ACL

• Wen Xiao and Giuseppe Carenini. 2019. Extractive Summarization of Long Documents by Combining Global and Local Context. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3011–3021, Hong Kong, China. Association for Computational Linguistics.