Embrace Divergence for Richer Insights: Multi-document Summarization Benchmark and Case Study

Concepts de base

LLMs struggle to cover diverse information effectively, highlighting the challenges in multi-document summarization.

Résumé

The paper introduces a new task of Multi-document Diversity Summarization (MDDS).
A dataset named DIVERSESUMM is created with 245 news stories, each containing 10 articles.
LLMs exhibit high faithfulness but struggle with coverage in summarizing diverse information.
Bias analysis reveals position and verbosity biases when using GPT-4 as an evaluator.
Correlation analysis suggests best practices for evaluating model performance in MDDS.
LLMs tend to summarize content from the first and last articles more than middle ones.
Coverage is higher for frequent answers, with long-context LLMs excelling at covering them.
Larger LLM models show improved coverage of diverse information.

Stats

LLMs struggle to achieve high coverage rates even with advanced models like GPT-4.
GPT-4 only covers about 37% of diverse information on average.
Faithfulness scores are high across different LLMs, but coverage remains a challenge.

Citations

Idées clés tirées de

Embrace Divergence for Richer Insights

by Kung-Hsiang ... à arxiv.org 03-26-2024

https://arxiv.org/pdf/2309.09369.pdf

Questions plus approfondies

How can the dataset DIVERSESUMM be expanded to include a wider range of news stories?

To expand the DIVERSESUMM dataset and include a wider range of news stories, several strategies can be implemented:

Increase Data Collection: Gather news stories from additional sources beyond Google News to ensure diversity in coverage.
Include Different Domains: Incorporate news articles from various domains such as politics, technology, health, and entertainment to broaden the scope of information.
Enhance Annotation Process: Implement more efficient methods for question generation and answer extraction to scale up data collection without compromising quality.
Collaborate with News Aggregators: Partner with other news aggregators or platforms to access a broader spectrum of articles and events.

What implications do the biases identified in GPT-4 evaluations have on the reliability of summarization results?

The biases identified in GPT-4 evaluations can significantly impact the reliability of summarization results:

Position Bias: The bias towards favoring certain positions over others may lead to skewed assessments, affecting the overall accuracy and fairness of summary evaluation.
Verbosity Bias: Preference for shorter summaries by GPT-4 could result in incomplete or insufficiently detailed summaries being favored over comprehensive ones, impacting content coverage.
Impact on Model Performance: Biases may influence model training and optimization processes, potentially leading to suboptimal performance if not appropriately addressed.

How can the findings on question types and answer frequency influence future developments in multi-document summarization?

The findings on question types and answer frequency offer valuable insights that can shape future advancements in multi-document summarization:

Optimized Summarization Strategies: Tailoring summarization approaches based on question types (e.g., focusing more on "Why" questions) can enhance coverage and relevance in generated summaries.
Improved Information Extraction Techniques: Understanding how LLMs handle frequent answers versus infrequent ones can guide researchers in developing algorithms that prioritize important but less common information for better summary quality.
Enhanced Evaluation Protocols: Insights into LLM behavior regarding diverse information identification pave the way for refining evaluation protocols that accurately assess faithfulness, coverage, and overall performance metrics in multi-document summarization tasks.