In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

Input: Consider Input: We evaluated our model on three benchmark datasets, namely the CNN/DailyMail news highlights dataset BIBREF24, the New York Times Annotated Corpus (NYT; BIBREF25), and XSum BIBREF22.

Output: What are the datasets used for evaluation?


Input: Consider Input: In this study, we focused on the LDA approaches to recommendation systems and given the importance of research, we have studied recent impressive articles on this subject and presented a taxonomy of recommendation systems based on LDA of the recent research. we evaluated ISWC and WWW conferences articles from DBLP website and used the Gibbs sampling algorithm as an evaluation parameter. We succeeded in discovering the relationship between LDA topics and paper features and also obtained the researchers' interest in research field. To perform approximate inference and learning LDA, there are many inference methods for LDA topic model such as Gibbs sampling, collapsed Variational Bayes, Expectation Maximization. Gibbs sampling is a popular technique because of its simplicity and low latency. However, for large numbers of topics, Gibbs sampling can become unwieldy. In this paper, we use Gibbs Sampling in our experiment in section 5.

Output: How they utilize LDA and Gibbs sampling to evaluate ISWC and WWW publications?


Input: Consider Input: We evaluate the TN topic model quantitatively with standard topic model measures such as test-set perplexity, likelihood convergence and clustering measures. Qualitatively, we evaluate the model by visualizing the topic summaries, authors' topic distributions and by performing an automatic labeling task.


Output: What are the measures of "performance" used in this paper?


