Enhancing Argument Summarization: Prioritizing Exhaustiveness in Key Point Generation and Introducing an Automatic Coverage Evaluation Metric (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing methods for summarizing arguments are incapable of distinguishing between generated key points of different qualities. |
| Approach: | They propose an extractive approach that generates concise, high quality key points . they propose to use a clustering approach to generate key points from raw arguments . |
| Outcome: | The proposed method outperforms state-of-the-art methods for key point generation . it offers concise, high quality generated key points with higher coverage of reference summaries . |
Similar Papers
Do You Hear The People Sing? Key Point Analysis via Iterative Clustering and Abstractive Summarisation (2023.acl-long)
Copied to clipboard
| Challenge: | Argument summarisation is a promising but currently under-explored field. |
| Approach: | They propose a framework to generate key points from short texts in a task known as Key Point Analysis. |
| Outcome: | The proposed framework improves state-of-the-art in argument summarisation with performance improvement of 14 percentage points compared to ROUGE and human evaluation scores. |
From Arguments to Key Points: Towards Automatic Argument Summarization (2020.acl-main)
Copied to clipboard
| Challenge: | Recent work on topic-related argument mining has made it difficult to read and digest large amounts of information. |
| Approach: | They propose to represent arguments as a small set of talking points, termed key points, each scored according to its salience. |
| Outcome: | The proposed method can predict key points in advance, and it performs well. |
Quantitative argument summarization and beyond: Cross-domain key point analysis (2020.emnlp-main)
Copied to clipboard
| Challenge: | Recent work on multi-document summarization lacks quantitative aspect of summarizing views, arguments or opinions . authors develop method for automatic extraction of key points, which is comparable to a human expert . |
| Approach: | They propose to map arguments to a small set of expert-generated key points . they demonstrate that the applicability of key point analysis goes well beyond argumentation data . |
| Outcome: | The proposed method outperforms arguments in municipal surveys and user reviews . it is shown that the extraction of key points is comparable to a human expert . |
Exploring Key Point Analysis with Pairwise Generation and Graph Partitioning (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing methods for key point analysis rely on semantic similarity instead of measuring the existence of shared key points . |
| Approach: | They propose a key point analysis approach with pairwise generation and graph partitioning to summarize arguments into a concise set of key points. |
| Outcome: | The proposed model surpasses existing models on ArgKP and QAM datasets. |
ArgCMV: An Argument Summarization Benchmark for the LLM-era (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for key point extraction are limited by the popular ArgKP21 dataset . a novel dataset for long-context online discussions is proposed . |
| Approach: | They propose to use a long-context argument key point extraction dataset to test this method. |
| Outcome: | The proposed dataset exhibits higher complexity, co-referencing arguments, higher presence of subjective discourse units, and a larger range of topics over the existing dataset. |
Welcome to the Real World: Efficient, Incremental and Scalable Key Point Analysis (2023.emnlp-industry)
Copied to clipboard
| Challenge: | Key Point Analysis (KPA) extracts the main points from opinions and quantifies their prevalence. |
| Approach: | They propose a key point analysis framework that extracts the main points from opinions and quantifies their prevalence. |
| Outcome: | The proposed system is able to match sentences to key points over five datasets and demonstrate its performance. |
From Key Points to Key Point Hierarchy: Structured and Expressive Opinion Summarization (2023.acl-long)
Copied to clipboard
| Challenge: | Key Point Analysis (KPA) is a new method for analyzing textual comments . it uses a list of concise sentences or phrases to extract key points from data . |
| Approach: | They propose to organize key points into a hierarchy according to their specificity . they compare methods for predicting pairwise relations between key points . |
| Outcome: | The proposed method improves on predicting pairwise key point relations and weak supervision. |
Aspect-based Key Point Analysis for Quantitative Summarization of Reviews (2024.findings-eacl)
Copied to clipboard
| Challenge: | Existing studies on review summarization use only major opinions, but ignore minority opinions and fail to quantify opinion prevalence. |
| Approach: | They propose a framework for quantitative review summarization using aspect-based key point analysis (ABKPA) they use aspect-basic sentiment analysis to automatically annotate silver labels for matching aspect-sentiment pairs . |
| Outcome: | The proposed framework outperforms state-of-the-art baselines on Yelp reviews on five business categories. |
An Empirical Analysis of Diversity in Argument Summarization (2024.eacl-long)
Copied to clipboard
| Challenge: | Current methods for summarizing arguments miss an important aspect of diversity . authors examine three aspects of diversity in argument summarization . |
| Approach: | They propose three aspects of diversity that are important for accommodating multiple perspectives. |
| Outcome: | The proposed models lack the diversity of opinions, sources, and annotators. |
Arg-LLaDA: Argument Summarization via Large Language Diffusion Models and Sufficiency-Aware Refinement (2026.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to argument summarization rely on single-pass generation, offering limited support for factual correction or structural refinement. |
| Approach: | They propose a large language diffusion framework that iteratively improves argument summarization by sufficiency-guided remasking and regeneration. |
| Outcome: | Empirical results show that Arg-LLaDA surpasses state-of-the-art baselines in 7 out of 10 evaluation metrics. |