# Quantitative Argument Summarization and Beyond: Cross-Domain Key Point Analysis

arXiv: https://arxiv.org/abs/2010.05369 · HTML: https://mlsanity.com/paper/2010.05369/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2020.

## Built on by (most cited first, 30 of 32)

- [ArgLegalSumm: Improving Abstractive Summarization of Legal Documents with Argument Mining](https://mlsanity.com/paper/2209.01650/) (2022)
- [Overview of the 2021 Key Point Analysis Shared Task](https://mlsanity.com/paper/2110.10577/) (2021)
- [Every Bite Is an Experience: Key Point Analysis of Business Reviews](https://mlsanity.com/paper/2106.06758/) (2021)
- [Scientia Potentia Est -- On the Role of Knowledge in Computational Argumentation](https://mlsanity.com/paper/2107.00281/) (2021)
- [Key Point Analysis via Contrastive Learning and Extractive Argument Summarization](https://mlsanity.com/paper/2109.15086/) (2021)
- [Examining Bias in Opinion Summarisation Through the Perspective of Opinion Diversity](https://mlsanity.com/paper/2306.04424/) (2023)
- [Which Side Are You On? A Multi-task Dataset for End-to-End Argument Summarisation and Evaluation](https://mlsanity.com/paper/2406.03151/) (2024)
- [Bias in Opinion Summarisation from Pre-training to Adaptation: A Case Study in Political Bias](https://mlsanity.com/paper/2402.00322/) (2024)
- [An Empirical Analysis of Diversity in Argument Summarization](https://mlsanity.com/paper/2402.01535/) (2024)
- [Project Debater APIs: Decomposing the AI Grand Challenge](https://mlsanity.com/paper/2110.01029/) (2021)
- [From Key Points to Key Point Hierarchy: Structured and Expressive Opinion Summarization](https://mlsanity.com/paper/2306.03853/) (2023)
- [A Hybrid Intelligence Method for Argument Mining](https://mlsanity.com/paper/2403.09713/) (2024)
- [Prompted Aspect Key Point Analysis for Quantitative Review Summarization](https://mlsanity.com/paper/2407.14049/) (2024)
- [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)
- [Benchmark Data and Evaluation Framework for Intent Discovery Around COVID-19 Vaccine Hesitancy](https://mlsanity.com/paper/2205.11966/) (2022)
- [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)
- [LLMs for Argument Mining: Detection, Extraction, and Relationship Classification of pre-defined Arguments in Online Comments](https://mlsanity.com/paper/2505.22956/) (2025)
- [Debatable Intelligence: Benchmarking LLM Judges via Debate Speech Evaluation](https://mlsanity.com/paper/2506.05062/) (2025)
- [CLEAR: Error Analysis via LLM-as-a-Judge Made Easy](https://mlsanity.com/paper/2507.18392/) (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)
- [Arguments to Key Points Mapping with Prompt-based Learning](https://mlsanity.com/paper/2211.14995/) (2022)
- [QANA: LLM-based Question Generation and Network Analysis for Zero-shot Key Point Analysis and Beyond](https://mlsanity.com/paper/2404.18371/) (2024)
- [From Judgement's Premises Towards Key Points](https://mlsanity.com/paper/2212.12238/) (2022)
- [Summarization from Leaderboards to Practice: Choosing A Representation Backbone and Ensuring Robustness](https://mlsanity.com/paper/2306.10555/) (2023)
- [Indicative Summarization of Long Discussions](https://mlsanity.com/paper/2311.01882/) (2023)
- [QQSUM: A Novel Task and Model of Quantitative Query-Focused Summarization for Review-based Product Question Answering](https://mlsanity.com/paper/2506.04020/) (2025)
- [Arg-LLaDA: Argument Summarization via Large Language Diffusion Models and Sufficiency-Aware Refinement](https://mlsanity.com/paper/2507.19081/) (2025)

## Abstract (arXiv)

When summarizing a collection of views, arguments or opinions on some topic, it is often desirable not only to extract the most salient points, but also to quantify their prevalence. Work on multi-document summarization has traditionally focused on creating textual summaries, which lack this quantitative aspect. Recent work has proposed to summarize arguments by mapping them to a small set of expert-generated key points, where the salience of each key point corresponds to the number of its matching arguments. The current work advances key point analysis in two important respects: first, we develop a method for automatic extraction of key points, which enables fully automatic analysis, and is shown to achieve performance comparable to a human expert. Second, we demonstrate that the applicability of key point analysis goes well beyond argumentation data. Using models trained on publicly available argumentation datasets, we achieve promising results in two additional domains: municipal surveys and user reviews. An additional contribution is an in-depth evaluation of argument-to-key point matching models, where we substantially outperform previous results.