RTSUM: Relation Triple-based Interpretable Summarization with Multi-level Salience Visualization
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866911016742289408 |
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| author | Cho, Seonglae Cho, Yonggi Lee, HoonJae Jang, Myungha Yeo, Jinyoung Lee, Dongha |
| author_facet | Cho, Seonglae Cho, Yonggi Lee, HoonJae Jang, Myungha Yeo, Jinyoung Lee, Dongha |
| contents | 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 codes,are publicly available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_13895 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | RTSUM: Relation Triple-based Interpretable Summarization with Multi-level Salience Visualization Cho, Seonglae Cho, Yonggi Lee, HoonJae Jang, Myungha Yeo, Jinyoung Lee, Dongha Computation and Language Machine Learning 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 codes,are publicly available. |
| title | RTSUM: Relation Triple-based Interpretable Summarization with Multi-level Salience Visualization |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2310.13895 |