Evaluating and Standardizing Summarization Datasets: Perspectives from a Comprehensive Survey
The research paper "The State and Fate of Summarization Datasets" provides an exhaustive survey of existing summarization datasets, including 133 datasets across more than 100 languages. Despite the increasing prominence of summarization tasks in NLP, the researchers argue that existing datasets suffer from a lack of standardization. In response, this work introduces a novel ontology to systematically classify summarization datasets based on multiple attributes such as language, domain, and collection methodology.
One core argument of the paper highlights the absence of consistent terminology and reporting standards, which complicates the discovery and comparison of summarization datasets. A particular issue is the traditional distinction between abstractive and extractive summarization models. The authors propose that this binary categorization no longer captures the complexities of current datasets and models, advocating instead for a spectrum of abstractiveness. Evidence from their analysis of novel n-gram ratios supports this perspective, revealing significant variability across datasets.
Another significant observation is the over-reliance on the news domain for dataset development. While news articles are readily accessible and often come with natural summaries, they can result in datasets that are limited in scope and quality. The researchers warn against this restriction, suggesting that it neither challenges current summarization models adequately nor supports the creation of high-quality, diverse datasets. Moreover, they identify legal complexities and copyright issues in sharing these datasets openly, a concern underscored by instances of datasets being retracted or legally scrutinized.
In terms of language diversity, while the number of languages represented in summarization datasets has widened — mainly due to the rise of multilingual resources — most non-English datasets are constrained to the news domain. This limited scope not only diminishes the quality of summaries in low-resource languages but also poses significant barriers in accessing relevant data for these languages.
The paper extends its analysis to annotation methods, noting a strong preference for using existing text as summaries rather than creating dedicated, task-specific annotations. This reliance on pre-existing summaries raises concerns about their validity for evaluating abstractive models and underscores the scarcity of large, human-annotated datasets.
As a potential contribution to the field, the authors presented two resources: an interactive web interface and a proposed summarization data card. The web interface is designed to serve as a centralized repository, assisting researchers in accessing datasets that meet their specific needs. The data card, inspired by model cards and data cards in other areas of machine learning, intends to set a standard for future dataset reporting. By combining a comprehensive classification approach with these resources, the authors aim to guide future dataset development and promote a coherent framework for reporting summarization datasets.
This paper's comprehensive survey and systematic ontology offer insightful contributions to the domain of automatic summarization. By addressing the identified challenges and promoting standardized practices, there is a clear pathway for future research to refine dataset quality and accessibility. Implementing these recommendations could significantly impact theoretical advancements and practical applications of summarization models, especially concerning the inclusion and treatment of low-resource languages in NLP research. As the summarization task continues to attract widespread attention, future developments may focus on further diversifying domains, refining annotation processes, and resolving legal distribution issues, contributing to the maturation of the field.