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:
Copy Citation:
PDF:
https://aclanthology.org/N19-1173.pdf
Code
 nlpyang/SUMO