@inproceedings{cho-etal-2024-rtsum,
    title = "{RTSUM}: Relation Triple-based Interpretable Summarization with Multi-level Salience Visualization",
    author = "Cho, Seonglae  and
      Jang, Myungha  and
      Yeo, Jinyoung  and
      Lee, Dongha",
    editor = "Chang, Kai-Wei  and
      Lee, Annie  and
      Rajani, Nazneen",
    booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: System Demonstrations)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.naacl-demo.5/",
    doi = "10.18653/v1/2024.naacl-demo.5",
    pages = "53--60",
    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."
}