Keyword-based Augmentation Method to Enhance Abstractive Summarization for Legal Documents

Author:

Nguyen Huyen1ORCID,Ding Junhua1ORCID

Affiliation:

1. University of North Texas, Denton, Texas, USA

Funder

NSF (National Science Foundation)

Publisher

ACM

Reference25 articles.

1. Deepa Anand and Rupali Wagh . 2019. Effective deep learning approaches for summarization of legal texts . Journal of King Saud University-Computer and Information Sciences ( 2019 ). Deepa Anand and Rupali Wagh. 2019. Effective deep learning approaches for summarization of legal texts. Journal of King Saud University-Computer and Information Sciences (2019).

2. Iz Beltagy , Matthew E Peters , and Arman Cohan . 2020 . Longformer: The long-document transformer. arXiv preprint arXiv:2004.05150 (2020). Iz Beltagy, Matthew E Peters, and Arman Cohan. 2020. Longformer: The long-document transformer. arXiv preprint arXiv:2004.05150 (2020).

3. Santosh Kumar Bharti and Korra Sathya Babu . 2017. Automatic keyword extraction for text summarization: A survey. arXiv preprint arXiv:1704.03242 ( 2017 ). Santosh Kumar Bharti and Korra Sathya Babu. 2017. Automatic keyword extraction for text summarization: A survey. arXiv preprint arXiv:1704.03242 (2017).

4. Florian Boudin and Emmanuel Morin. 2013. Keyphrase extraction for n-best reranking in multi-sentence compression. In North American Chapter of the Association for Computational Linguistics (NAACL). Florian Boudin and Emmanuel Morin. 2013. Keyphrase extraction for n-best reranking in multi-sentence compression. In North American Chapter of the Association for Computational Linguistics (NAACL).

5. A Comparison of Supervised Keyphrase Extraction Models

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