Enhancing Argument Summarization: Generating Concise and Comprehensive Key Points with High Coverage

Core Concepts

A novel extractive approach for key point generation that outperforms previous state-of-the-art methods by generating concise, high-quality key points with higher coverage of reference summaries and less redundant outputs.

Abstract

The paper introduces a novel extractive approach for key point generation (KPG) in the context of argument summarization. The key contributions are:

An extractive clustering-based framework for KPG that generates concise, high-quality key points with higher coverage of reference summaries and less redundant outputs compared to previous state-of-the-art methods.

The framework first clusters similar arguments using fine-tuned Sentence-BERT embeddings and Agglomerative Clustering. It then selects the most representative argument from each cluster as the key point, using a key point matching (KPM) model.

Two selection methods are proposed - Selection with Matching Model (SMM) and Selection with Scoring Function (SSF). SMM selects the argument with the highest number of matches within the cluster, while SSF also considers the length of the argument.

The paper also introduces a new automatic evaluation metric for the KPG task, which measures the coverage of reference key points by the generated key points. This metric is shown to better correlate with the actual key point coverage compared to existing metrics like ROUGE.

Experiments on the ArgKP and Debate datasets demonstrate the effectiveness of the proposed approach in generating more exhaustive and less redundant key points compared to previous methods. The human evaluation also shows that the generated key points are more concise and understandable.

Stats

"Routine child vaccinations, or their side effects, are potentially dangerous."
"The decision of vaccinating a child should rest with parents, not the state."
"People all around the world vaccinate their children to protect them from any life threatening disease."
"Parents should be allowed to choose if their child is vaccinated or not."

Quotes

"A key point (KP) should be a concise, high-level argument that aligns with a significant facet of a recurrent argument, while still being informative."
"The goal of KPG is to generate the key points, given the arguments as inputs."
"Our method utilizes an extractive clustering based approach that offers concise, high quality generated key points with higher coverage of reference summaries, and less redundant outputs."

Key Insights Distilled From

Enhancing Argument Summarization: Prioritizing Exhaustiveness in Key Point Generation and Introducing an Automatic Coverage Evaluation Metric

by Mohammad Kho... at arxiv.org 04-19-2024

https://arxiv.org/pdf/2404.11793.pdf

Deeper Inquiries

How can the proposed approach be extended to handle hierarchical relationships between arguments and key points?

In order to handle hierarchical relationships between arguments and key points, the proposed approach can be enhanced by incorporating a more sophisticated clustering algorithm that can identify and group arguments based on their hierarchical structure. This could involve implementing a multi-level clustering approach where arguments are first clustered at a broader level based on general themes or topics, and then further clustered into subgroups based on more specific aspects or subtopics. By structuring the clustering process in this hierarchical manner, the method can capture the nuanced relationships between arguments and key points, ensuring that the generated summaries reflect the hierarchical organization of the arguments.
Additionally, the method can utilize techniques from hierarchical clustering algorithms such as agglomerative clustering with different linkage criteria (e.g., complete linkage, average linkage) to capture the hierarchical relationships effectively. By considering the hierarchical nature of arguments and key points, the method can generate summaries that not only cover a wide range of key points but also reflect the nuanced relationships and dependencies between different aspects of the arguments.

How can the method be adapted to ensure balanced coverage of different stances or viewpoints in the generated summary?

To ensure balanced coverage of different stances or viewpoints in the generated summary, the method can be adapted by incorporating a stance detection component that identifies the stance or viewpoint of each argument. By integrating a stance detection model into the clustering process, the method can group arguments based on their stance, ensuring that the generated summaries include key points from diverse perspectives.
Furthermore, the method can implement a weighting mechanism that assigns higher importance to arguments representing minority or less popular stances. This can be achieved by adjusting the clustering algorithm to prioritize the inclusion of arguments from underrepresented stances or viewpoints in the generated summaries. By giving equal consideration to arguments from all stances, the method can ensure a balanced coverage of different viewpoints in the summary.
Additionally, the method can introduce a post-processing step that evaluates the distribution of key points across different stances in the generated summary and adjusts the output to achieve a more balanced representation of diverse viewpoints. By actively monitoring and adjusting the coverage of different stances, the method can generate summaries that reflect a fair and comprehensive overview of the arguments presented.

What other applications beyond argument summarization could benefit from the proposed extractive clustering-based approach for generating concise and comprehensive summaries?

The extractive clustering-based approach for generating concise and comprehensive summaries can be applied to various other domains and applications beyond argument summarization. Some potential applications include:

Document Summarization: The method can be utilized for summarizing long documents, research papers, or reports by clustering and extracting key points to provide a concise overview of the content.

Social Media Analysis: In social media analytics, the approach can be used to summarize discussions, comments, or reviews on platforms like Twitter, Facebook, or Reddit to extract key insights and trends.

Market Research: The method can assist in summarizing customer feedback, surveys, or product reviews to identify common themes, sentiments, and feedback for market research purposes.

Legal Document Analysis: In the legal domain, the approach can be applied to summarize legal cases, contracts, or court proceedings to extract key arguments, decisions, and important details.

Healthcare Data Analysis: The method can help in summarizing medical records, patient histories, or research studies to extract key findings, trends, and insights for healthcare professionals and researchers.

By leveraging the extractive clustering-based approach for summarization, these applications can benefit from the generation of concise, informative, and comprehensive summaries that capture the essential information and insights from large volumes of text data.