From Judgement's Premises Towards Key Points
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Abstract (arXiv)
Key Point Analysis(KPA) is a relatively new task in NLP that combines summarization and classification by extracting argumentative key points (KPs) for a topic from a collection of texts and categorizing their closeness to the different arguments. In our work, we focus on the legal domain and develop methods that identify and extract KPs from premises derived from texts of judgments. The first method is an adaptation to an existing state-of-the-art method, and the two others are new methods that we developed from scratch. We present our methods and examples of their outputs, as well a comparison between them. The full evaluation of our results is done in the matching task -- match between the generated KPs to arguments (premises).
Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 9 of its 16 references that are in the map. First posted on arXiv 23 Dec 2022.
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References · 9
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,304
- 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,884
- 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,100
- 2020LEGAL-BERT: The Muppets straight out of Law Schoolcited by 111
- 2020From Arguments to Key Points: Towards Automatic Argument Summarizationcited by 60
- 2020Quantitative Argument Summarization and Beyond: Cross-Domain Key Point Analysiscited by 31
- 2021Overview of the 2021 Key Point Analysis Shared Taskcited by 23
- 2021Key Point Analysis via Contrastive Learning and Extractive Argument Summarizationcited by 16
- 2021Team Enigma at ArgMining-EMNLP 2021: Leveraging Pre-trained Language Models for Key Point Matchingcited by 11
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