@inproceedings{xiao-etal-2021-predicting,
title = "Predicting Discourse Trees from Transformer-based Neural Summarizers",
author = "Xiao, Wen and
Huber, Patrick and
Carenini, Giuseppe",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.326",
doi = "10.18653/v1/2021.naacl-main.326",
pages = "4139--4152",
abstract = "Previous work indicates that discourse information benefits summarization. In this paper, we explore whether this synergy between discourse and summarization is bidirectional, by inferring document-level discourse trees from pre-trained neural summarizers. In particular, we generate unlabeled RST-style discourse trees from the self-attention matrices of the transformer model. Experiments across models and datasets reveal that the summarizer learns both, dependency- and constituency-style discourse information, which is typically encoded in a single head, covering long- and short-distance discourse dependencies. Overall, the experimental results suggest that the learned discourse information is general and transferable inter-domain.",
}
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%0 Conference Proceedings
%T Predicting Discourse Trees from Transformer-based Neural Summarizers
%A Xiao, Wen
%A Huber, Patrick
%A Carenini, Giuseppe
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 jun
%I Association for Computational Linguistics
%C Online
%F xiao-etal-2021-predicting
%X Previous work indicates that discourse information benefits summarization. In this paper, we explore whether this synergy between discourse and summarization is bidirectional, by inferring document-level discourse trees from pre-trained neural summarizers. In particular, we generate unlabeled RST-style discourse trees from the self-attention matrices of the transformer model. Experiments across models and datasets reveal that the summarizer learns both, dependency- and constituency-style discourse information, which is typically encoded in a single head, covering long- and short-distance discourse dependencies. Overall, the experimental results suggest that the learned discourse information is general and transferable inter-domain.
%R 10.18653/v1/2021.naacl-main.326
%U https://aclanthology.org/2021.naacl-main.326
%U https://doi.org/10.18653/v1/2021.naacl-main.326
%P 4139-4152
Markdown (Informal)
[Predicting Discourse Trees from Transformer-based Neural Summarizers](https://aclanthology.org/2021.naacl-main.326) (Xiao et al., NAACL 2021)
ACL