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Abstract / Description of output
In this paper we conceptualize single-document extractive summarization as a tree induction problem. In contrast to previous approaches (Marcu, 1999; Yoshida et al., 2014) which have relied on linguistically motivated document representations to generate summaries, our model induces a multi-root dependency tree while predicting the output summary. Each root node in the tree is a summary sentence, and the subtrees attached to it are sentences whose content relates to or explains the summary sentence. We design a new iterative refinement algorithm: it induces the trees through repeatedly refining the structures predicted by previous iterations. We demonstrate experimentally on two benchmark datasets that our summarizer1 performs competitively against state-of-the-art methods.
1: Our code is publicly available at https://github.com/nlpyang/SUMO.
1: Our code is publicly available at https://github.com/nlpyang/SUMO.
Original language | English |
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Title of host publication | Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics |
Editors | Jill Burstein, Christy Doran, Thamar Solorio |
Place of Publication | Minneapolis, Minnesota |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 1745–1755 |
Number of pages | 11 |
Volume | 1 |
DOIs | |
Publication status | Published - 7 Jun 2019 |
Event | 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics - Minneapolis, United States Duration: 2 Jun 2019 → 7 Jun 2019 https://naacl2019.org/ |
Conference
Conference | 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics |
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Abbreviated title | NAACL-HLT 2019 |
Country/Territory | United States |
City | Minneapolis |
Period | 2/06/19 → 7/06/19 |
Internet address |
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