Kleo/ArgKP_2021_GR

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The declared licence (apache-2.0) is classified as permissive.

Declared licence: apache-2.0, from the tagEvidence: README.md at 65e913f
Licence files: No licence file at the top level of the repositoryEvidence: file list at 65e913f
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Citation text from the card

@inproceedings{bar-haim-etal-2020-arguments,
    title = "From Arguments to Key Points: {T}owards Automatic Argument Summarization",
    author = "Bar-Haim, Roy  and
      Eden, Lilach  and
      Friedman, Roni  and
      Kantor, Yoav  and
      Lahav, Dan  and
      Slonim, Noam",
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.acl-main.371/",
    doi = "10.18653/v1/2020.acl-main.371",
    pages = "4029--4039",
    abstract = "Generating a concise summary from a large collection of arguments on a given topic is an intriguing yet understudied problem. We propose to represent such summaries as a small set of talking points, termed \textit{key points}, each scored according to its salience. We show, by analyzing a large dataset of crowd-contributed arguments, that a small number of key points per topic is typically sufficient for covering the vast majority of the arguments. Furthermore, we found that a domain expert can often predict these key points in advance. We study the task of argument-to-key point mapping, and introduce a novel large-scale dataset for this task. We report empirical results for an extensive set of experiments with this dataset, sho

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dataset_licence({ "id": "Kleo/ArgKP_2021_GR" })
signal: "permissive"
stated_licence: "apache-2.0"
sources: 0