Determining Sample Size for Summarization Model Comparison

Core Concepts

The author empirically investigates the necessary test sample size to select a preferred summarization model, revealing that preferences emerge from under 100 examples. They validate various evaluation methods and propose a new approach to validating automatic evaluations.

Abstract

In this study, the authors explore the minimum data required to compare summarization models effectively. They find that preferences for models emerge quickly with a small sample size of under 100 examples. Human preferences vary based on task context and input source, highlighting the need for new methods to validate automatic evaluations. The study provides insights into the efficiency of different evaluation metrics in predicting human preferences and emphasizes the importance of context in determining model performance.

Stats

Comparative evaluation converges quickly for both automatic and human evaluation, with clear preferences emerging from under 100 examples.
The winning model as scored over 10k test points emerges after just 25-50 samples.
For human evaluation, a test size of 50 is sufficient to confidently establish which model people prefer.
ROUGE-1 and GPT-4 as an annotator are able to moderately predict aggregated human preferences across different tasks.

Quotes

"Preferences toward a summarization model emerge over test sets of about 50 samples."
"Human preference varies depending on intended use of summaries and source of data."

Key Insights Distilled From

How Much Annotation is Needed to Compare Summarization Models?

by Chantal Shai... at arxiv.org 03-01-2024

https://arxiv.org/pdf/2402.18756.pdf

Deeper Inquiries

How do varying task contexts impact human preferences for summarization models?

In the context of the study on comparing summarization models, varying task contexts have a significant impact on human preferences. The research revealed that depending on the specific use-case or intended purpose of the summary, human preferences for different summarization models can change dramatically. For example, when annotators were asked to rank summaries based on monitoring important world events or identifying main details in an article, their preferences for certain models shifted compared to a general ranking scenario.
These findings suggest that the relevance and applicability of a summary play a crucial role in determining which model is preferred by humans. Different tasks require different aspects of a summary to be emphasized, leading to variations in preference among annotators. Therefore, understanding how task contexts influence human judgments is essential when evaluating and selecting summarization models for specific applications.

How can the research on sample sizes for comparing summarization models be extended beyond news datasets?

The study's focus on determining the minimum sample size needed to compare summarization models can be extended beyond news datasets by exploring diverse domains and text genres. To broaden the scope of this research:

Domain-specific Evaluation: Conduct similar experiments using datasets from various domains such as scientific articles, legal documents, social media posts, or medical reports. Understanding how sample sizes vary across different domains will provide insights into domain-specific requirements for model evaluation.

Multimodal Summarization: Investigate sample size requirements for multimodal summarization tasks involving text along with images or videos. Assessing how incorporating multiple modalities impacts sample size needs will enhance our understanding of comprehensive content summarization.

Cross-lingual Summarization: Explore cross-lingual settings where summaries need to be generated in multiple languages from multilingual inputs. Analyzing sample size effects in cross-lingual scenarios will shed light on challenges and opportunities unique to language-diverse environments.

Fine-grained Evaluation Metrics: Introduce additional evaluation metrics beyond ROUGE and BERTScore tailored to specific domains or tasks like sentiment analysis accuracy or factual correctness assessment within summaries.

By expanding research into these areas, we can gain deeper insights into optimal sample sizes required for evaluating summarization models across diverse data types and applications.