# Key Point Analysis via Contrastive Learning and Extractive Argument Summarization

arXiv: https://arxiv.org/abs/2109.15086 · HTML: https://mlsanity.com/paper/2109.15086/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 25 references that are in the map.
- First posted on arXiv 30 Sep 2021.

## Builds on (most cited first, 11 of 11)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [From Arguments to Key Points: Towards Automatic Argument Summarization](https://mlsanity.com/paper/2005.01619/) (2020)
- [Automatic Argument Quality Assessment -- New Datasets and Methods](https://mlsanity.com/paper/1909.01007/) (2019)
- [Neural Network-Based Abstract Generation for Opinions and Arguments](https://mlsanity.com/paper/1606.02785/) (2016)
- [Aspect-Controlled Neural Argument Generation](https://mlsanity.com/paper/2005.00084/) (2020)
- [What is the Essence of a Claim? Cross-Domain Claim Identification](https://mlsanity.com/paper/1704.07203/) (2017)
- [Quantitative Argument Summarization and Beyond: Cross-Domain Key Point Analysis](https://mlsanity.com/paper/2010.05369/) (2020)
- [Measuring the Similarity of Sentential Arguments in Dialog](https://mlsanity.com/paper/1709.01887/) (2017)
- [Generating Informative Conclusions for Argumentative Texts](https://mlsanity.com/paper/2106.01064/) (2021)

## Built on by (most cited first, 16 of 16)

- [Overview of the 2021 Key Point Analysis Shared Task](https://mlsanity.com/paper/2110.10577/) (2021)
- [Large Language Models in Argument Mining: A Survey](https://mlsanity.com/paper/2506.16383/) (2025)
- [Which Side Are You On? A Multi-task Dataset for End-to-End Argument Summarisation and Evaluation](https://mlsanity.com/paper/2406.03151/) (2024)
- [An Empirical Analysis of Diversity in Argument Summarization](https://mlsanity.com/paper/2402.01535/) (2024)
- [Will It Blend? Mixing Training Paradigms & Prompting for Argument Quality Prediction](https://mlsanity.com/paper/2209.08966/) (2022)
- [Do You Hear The People Sing? Key Point Analysis via Iterative Clustering and Abstractive Summarisation](https://mlsanity.com/paper/2305.16000/) (2023)
- [Enhancing Argument Summarization: Prioritizing Exhaustiveness in Key Point Generation and Introducing an Automatic Coverage Evaluation Metric](https://mlsanity.com/paper/2404.11793/) (2024)
- [Exploring Key Point Analysis with Pairwise Generation and Graph Partitioning](https://mlsanity.com/paper/2404.11384/) (2024)
- [Argument Summarization and its Evaluation in the Era of Large Language Models](https://mlsanity.com/paper/2503.00847/) (2025)
- [ArgCMV: An Argument Summarization Benchmark for the LLM-era](https://mlsanity.com/paper/2508.19580/) (2025)
- [Hi-ArG: Exploring the Integration of Hierarchical Argumentation Graphs in Language Pretraining](https://mlsanity.com/paper/2312.00874/) (2023)
- [From Judgement's Premises Towards Key Points](https://mlsanity.com/paper/2212.12238/) (2022)
- [Cone: Unsupervised Contrastive Opinion Extraction](https://mlsanity.com/paper/2305.04599/) (2023)
- [Arg-LLaDA: Argument Summarization via Large Language Diffusion Models and Sufficiency-Aware Refinement](https://mlsanity.com/paper/2507.19081/) (2025)
- [A Community-Based Approach for Stance Distribution and Argument Organization](https://mlsanity.com/paper/2604.16852/) (2026)
- [Key Point Analysis Needs Structure Recovery: Task Definition, Dataset Diagnosis, and a Structure-Aware Benchmark](https://mlsanity.com/paper/2608.25854/) (2026)

## 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.