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Key Point Analysis via Contrastive Learning and Extractive Argument Summarization

arXiv 2109.15086 ↗First posted 30 Sep 2021cs.CLcited by 16

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Abstract (arXiv)

Key point analysis is the task of extracting a set of concise and high-level statements from a given collection of arguments, representing the gist of these arguments. This paper presents our proposed approach to the Key Point Analysis shared task, collocated with the 8th Workshop on Argument Mining. The approach integrates two complementary components. One component employs contrastive learning via a siamese neural network for matching arguments to key points; the other is a graph-based extractive summarization model for generating key points. In both automatic and manual evaluation, our approach was ranked best among all submissions to the shared task.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 11 of its 25 references that are in the map. First posted on arXiv 30 Sep 2021.

Builds on

11 of 25 references.

References · 11

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  2. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  3. 2019Text Summarization with Pretrained Encoderscited by 553
  4. 2020From Arguments to Key Points: Towards Automatic Argument Summarizationcited by 60
  5. 2019Automatic Argument Quality Assessment -- New Datasets and Methodscited by 47
  6. 2016Neural Network-Based Abstract Generation for Opinions and Argumentscited by 46
  7. 2020Aspect-Controlled Neural Argument Generationcited by 43
  8. 2017What is the Essence of a Claim? Cross-Domain Claim Identificationcited by 31
  9. 2020Quantitative Argument Summarization and Beyond: Cross-Domain Key Point Analysiscited by 31
  10. 2017Measuring the Similarity of Sentential Arguments in Dialogcited by 23
  11. 2021Generating Informative Conclusions for Argumentative Textscited by 14

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 3; 2024: 4; 2025: 4; 2026: 2.

16 papers.

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  1. 2021Overview of the 2021 Key Point Analysis Shared Taskcited by 23
  2. 2025Large Language Models in Argument Mining: A Surveycited by 14
  3. 2024Which Side Are You On? A Multi-task Dataset for End-to-End Argument Summarisation and Evaluationcited by 13
  4. 2024An Empirical Analysis of Diversity in Argument Summarizationcited by 9
  5. 2022Will It Blend? Mixing Training Paradigms & Prompting for Argument Quality Predictioncited by 7
  6. 2023Do You Hear The People Sing? Key Point Analysis via Iterative Clustering and Abstractive Summarisationcited by 5
  7. 2024Enhancing Argument Summarization: Prioritizing Exhaustiveness in Key Point Generation and Introducing an Automatic Coverage Evaluation Metriccited by 5
  8. 2024Exploring Key Point Analysis with Pairwise Generation and Graph Partitioningcited by 4
  9. 2025Argument Summarization and its Evaluation in the Era of Large Language Modelscited by 4
  10. 2025ArgCMV: An Argument Summarization Benchmark for the LLM-eracited by 3
  11. 2023Hi-ArG: Exploring the Integration of Hierarchical Argumentation Graphs in Language Pretrainingcited by 2
  12. 2022From Judgement's Premises Towards Key Pointscited by 0
4 more
  1. 2023Cone: Unsupervised Contrastive Opinion Extractioncited by 0
  2. 2025Arg-LLaDA: Argument Summarization via Large Language Diffusion Models and Sufficiency-Aware Refinementcited by 0
  3. 2026A Community-Based Approach for Stance Distribution and Argument Organizationcited by 0
  4. 2026Key Point Analysis Needs Structure Recovery: Task Definition, Dataset Diagnosis, and a Structure-Aware Benchmarkcited by 0