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    <abstract>In this paper, we present RTSum, an unsupervised summarization framework that utilizes relation triples as the basic unit for summarization. Given an input document, RTSum first selects salient relation triples via multi-level salience scoring and then generates a concise summary from the selected relation triples by using a text-to-text language model. On the basis of RTSum, we also develop a web demo for an interpretable summarizing tool, providing fine-grained interpretations with the output summary. With support for customization options, our tool visualizes the salience for textual units at three distinct levels: sentences, relation triples, and phrases. The code, demo, and video are publicly available.</abstract>
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%0 Conference Proceedings
%T RTSUM: Relation Triple-based Interpretable Summarization with Multi-level Salience Visualization
%A Cho, Seonglae
%A Jang, Myungha
%A Yeo, Jinyoung
%A Lee, Dongha
%Y Chang, Kai-Wei
%Y Lee, Annie
%Y Rajani, Nazneen
%S Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: System Demonstrations)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F cho-etal-2024-rtsum
%X In this paper, we present RTSum, an unsupervised summarization framework that utilizes relation triples as the basic unit for summarization. Given an input document, RTSum first selects salient relation triples via multi-level salience scoring and then generates a concise summary from the selected relation triples by using a text-to-text language model. On the basis of RTSum, we also develop a web demo for an interpretable summarizing tool, providing fine-grained interpretations with the output summary. With support for customization options, our tool visualizes the salience for textual units at three distinct levels: sentences, relation triples, and phrases. The code, demo, and video are publicly available.
%R 10.18653/v1/2024.naacl-demo.5
%U https://aclanthology.org/2024.naacl-demo.5
%U https://doi.org/10.18653/v1/2024.naacl-demo.5
%P 53-60

Markdown (Informal)

[RTSUM: Relation Triple-based Interpretable Summarization with Multi-level Salience Visualization](https://aclanthology.org/2024.naacl-demo.5) (Cho et al., NAACL 2024)

• RTSUM: Relation Triple-based Interpretable Summarization with Multi-level Salience Visualization (Cho et al., NAACL 2024)

ACL

• Seonglae Cho, Myungha Jang, Jinyoung Yeo, and Dongha Lee. 2024. RTSUM: Relation Triple-based Interpretable Summarization with Multi-level Salience Visualization. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: System Demonstrations), pages 53–60, Mexico City, Mexico. Association for Computational Linguistics.