Single Document Summarization as Tree Induction

Yang Liu, Ivan Titov, Mirella Lapata


Abstract
In this paper, we conceptualize single-document extractive summarization as a tree induction problem. In contrast to previous approaches 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 summarizer performs competitively against state-of-the-art methods.
Anthology ID:
N19-1173
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1745–1755
Language:
URL:
https://aclanthology.org/N19-1173
DOI:
10.18653/v1/N19-1173
Bibkey:
Cite (ACL):
Yang Liu, Ivan Titov, and Mirella Lapata. 2019. Single Document Summarization as Tree Induction. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1745–1755, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Single Document Summarization as Tree Induction (Liu et al., NAACL 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/update-css-js/N19-1173.pdf
Code
 nlpyang/SUMO