### Abstract
This survey paper provides a comprehensive overview of the evaluation metrics used for Natural Language Generation (NLG) systems, synthesizing findings from 100 influential research papers published over the past decade. The paper highlights key advancements, methodologies, and challenges, offering insights into future research directions. It emphasizes the importance of developing robust and context-aware evaluation metrics that can effectively capture the nuances of NLG outputs, and it underscores the need for a multifaceted approach that integrates human evaluations with automated metrics.

### Introduction
The rapid evolution of Natural Language Generation (NLG) systems has led to significant advancements in areas such as image captioning, text summarization, dialogue systems, and content generation. As these systems become increasingly sophisticated, the need for reliable and meaningful evaluation metrics becomes paramount. Traditional evaluation metrics, such as BLEU and ROUGE, have been widely used but are often found to be inadequate for capturing the complexities of NLG outputs. This survey aims to consolidate knowledge from a vast array of studies to provide researchers with a coherent understanding of the current landscape of NLG evaluation metrics. It addresses the importance of developing metrics that are theoretically sound, practically applicable, and contextually appropriate, while also highlighting the ongoing challenges and future directions in the field.

### Main Sections

#### Overview of Evaluation Metrics in NLG Systems
The evaluation of NLG systems is a complex task that requires a nuanced approach due to the variability and complexity of natural language. Traditional metrics, such as BLEU and ROUGE, have been extensively used for tasks like machine translation and text summarization. However, these metrics often fall short in capturing the full spectrum of NLG output quality, including fluency, coherence, informativeness, and relevance. This section provides an overview of the common evaluation metrics used in NLG systems and discusses their strengths and limitations.

##### Strengths and Limitations of Traditional Metrics
Traditional metrics like BLEU and ROUGE are based on lexical overlap and statistical measures, which are computationally efficient and easy to implement. However, they are often criticized for their inability to capture semantic meaning, syntactic correctness, and contextual appropriateness. For instance, BLEU rewards exact matches, which may not always reflect the quality of the generated text. Similarly, ROUGE focuses on n-gram overlaps, which can lead to high scores for poorly structured or irrelevant text.

##### Emerging Trends in Evaluation Metrics
Recent research has shifted towards developing more sophisticated metrics that can better reflect the quality of NLG outputs. These metrics often incorporate deep learning models and leverage large language models (LLMs) to capture deeper semantic and stylistic qualities. For example, some studies have proposed metrics derived from LLMs that can provide more nuanced assessments of NLG outputs, reflecting a closer alignment with human judgment.

#### Methodologies and Approaches
The methodologies used in evaluating NLG systems vary widely, reflecting the diversity of applications and evaluation goals. This section discusses the common methodologies and approaches employed in NLG evaluation, including human evaluations, automated metrics, benchmarking frameworks, and behavioral testing.

##### Human Evaluations
Human evaluations are considered the gold standard for assessing NLG systems, as they can capture the subjective aspects of text quality that automated metrics may miss. However, human evaluations are resource-intensive and subject to bias. Recent studies have explored ways to enhance the efficiency and objectivity of human evaluations, such as using dueling bandit algorithms to identify top-performing systems with minimal human intervention.

##### Automated Metrics
Automated metrics are scalable and can provide quick assessments of NLG systems. These metrics often rely on heuristics or machine learning models to evaluate the quality of generated text. For instance, some studies have proposed metrics that leverage LLMs to capture deeper semantic and stylistic qualities of NLG outputs. Automated metrics are particularly useful for large-scale evaluations and can complement human evaluations by providing objective measurements.

##### Benchmarking Frameworks
Benchmarking frameworks provide standardized platforms for evaluating NLG systems across various tasks and datasets. These frameworks facilitate fair comparisons and track improvements over time. For example, the MultiMedEval benchmark and the GEMv2 framework provide comprehensive platforms for evaluating NLG systems in medical and general domains, respectively. Benchmarking frameworks are essential for ensuring the reproducibility and comparability of evaluation results.

##### Behavioral Testing
Behavioral testing involves simulating real-world behaviors to evaluate NLG systems. This approach helps in identifying practical issues that may not be captured by traditional metrics. For instance, behavioral testing can assess the robustness of NLG systems in handling adversarial attacks or out-of-distribution samples. Behavioral testing is particularly important for safety-critical applications where the performance of NLG systems can have significant real-world consequences.

#### Common Themes and Trends
Several recurring themes emerge from the surveyed papers, reflecting the current state and future directions of NLG evaluation.

##### Human Evaluation vs. Automated Metrics
There is ongoing debate about the relative merits of human evaluations versus automated metrics. While human evaluations are considered the gold standard, they are resource-intensive and subjective. Automated metrics, on the other hand, are scalable but may suffer from biases and lack of interpretability. Future research should focus on developing hybrid approaches that combine the strengths of both human and automated evaluations.

##### Contextual Evaluation
Evaluation methodologies must consider the specific context in which NLG systems operate. For instance, medical applications may require stricter evaluation criteria compared to general text generation tasks. Contextual evaluation is crucial for ensuring that NLG systems meet the specific requirements and constraints of different applications.

##### Robustness Against Data Contamination
Given the potential for data contamination in evaluations, especially with models like ChatGPT, several papers discuss the importance of ensuring that evaluation data is representative and unbiased. This includes developing robust evaluation methods that can handle imbalanced data distributions and out-of-distribution samples.

##### Development of Context-Specific Metrics
Further research should focus on developing evaluation metrics tailored to specific NLG tasks and applications. For example, metrics designed for summarization might prioritize brevity and informativeness, whereas those for dialogue systems might emphasize conversational fluency. Context-specific metrics can provide more accurate and meaningful evaluations of NLG systems.

#### Key Contributions and Innovations
Several studies have made significant contributions to the field of NLG evaluation, introducing new methodologies and metrics that enhance the reliability and validity of evaluations.

##### Enhanced Evaluation Frameworks
Studies like "Deconstructing NLG Evaluation" by Zhou et al. identify practitioner goals, assumptions, and constraints that shape NLG evaluations, providing a framework for more informed and ethical evaluation practices. These frameworks help in designing evaluation methods that are contextually appropriate and ethically sound.

##### Efficient Evaluation Techniques
Techniques such as Active Evaluation by Mohankumar and Khapra propose frameworks that use dueling bandit algorithms to reduce the number of human annotations required for NLG evaluation, thereby making the process more efficient. These techniques can significantly enhance the scalability and efficiency of NLG evaluations.

##### New Metrics and Benchmarks
Papers like "Revisiting Long-tailed Image Classification" by Fang et al. introduce novel evaluation metrics for long-tailed image classification, which can be adapted for NLG systems dealing with imbalanced data distributions. These new metrics and benchmarks provide more comprehensive and context-aware evaluations of NLG systems.

##### Addressing Limitations
Studies like "Defining and Detecting Vulnerability" by Ruan et al. identify vulnerabilities in existing evaluation guidelines and propose methods to enhance their reliability, ensuring more accurate and reproducible evaluations. Addressing these limitations is crucial for advancing the field of NLG evaluation.

#### Challenges and Limitations
Despite significant advancements, several challenges remain in the field of NLG evaluation. These challenges include the variability in metric performance across different tasks and datasets, the difficulty in accurately assessing novel and relevant recommendations due to missing data in historical records, and the need for more robust offline evaluation techniques.

##### Variability in Metric Performance
Metric performance is highly dependent on the specific NLG system and dataset being evaluated. This variability underscores the need for careful consideration when selecting or designing metrics for specific applications. Future research should focus on developing more generalizable and context-aware metrics that can perform consistently across different tasks and datasets.

##### Difficulty in Accurate Assessments
Accurately assessing novel and relevant recommendations is challenging due to missing data in historical records. This issue is particularly prevalent in safety-critical applications where the performance of NLG systems can have significant real-world consequences. Developing more robust offline evaluation techniques is essential for addressing this challenge.

##### Ethical Considerations
Ethical considerations in evaluation practices, such as fairness and transparency, should be prioritized to ensure the responsible development and deployment of NLG systems. Ensuring that evaluation methods are transparent, fair, and unbiased is crucial for building trust in NLG systems.

### Conclusion
This survey has provided a comprehensive overview of the current state of evaluation metrics used for NLG systems, highlighting key contributions, methodologies, and implications from 100 influential research papers. The studies collectively emphasize the importance of developing metrics that are both theoretically sound and practically applicable. Future research should continue to explore innovative approaches and address the ongoing challenges in NLG evaluation. By doing so, the field can move closer to achieving reliable and meaningful assessments of NLG systems, ultimately driving their advancement and adoption in real-world applications.

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