Summarization Datasets for Research: A Survey

In this article, the authors explore the use of commonly available summarization datasets from HuggingFace for training and evaluating summarization models. They analyze the performance of two popular models, BART (Xsum) and NV-BART (Xsum), on four datasets: CNN/DailyMail, Xsum, Curation Corpus, and SAMsum. The authors find that NV-BART (Xsum) outperforms BART (Xsum) on three out of the four datasets, producing longer and more accurate summaries that are better adapted to the information in the document.
The authors also compare the performance of NV-BART (Xsum) with the baseline model on the SAMsum and WikiHow datasets, finding that it produces shorter and more accurate summaries that are more adaptive to the information in the document.
To demystify complex concepts, the authors use everyday language and engaging metaphors or analogies to explain the findings. For example, they describe the difference between BART (Xsum) and NV-BART (Xsum) as "like using a map to navigate versus using a GPS with real-time traffic updates."
Overall, the article provides valuable insights into the performance of different summarization models on various datasets and highlights the importance of selecting appropriate datasets for training and evaluating summarization systems. The authors’ use of clear and concise language makes the article accessible to a wide readership, including those without prior knowledge of summarization or machine learning.