instruction:
In this task, you will be presented with a context from an academic paper and a question based on the context. You have to classify the questions into "Extractive", "Abstractive", or "Yes-no" questions. Extractive questions can be answered by concatenating extracts taken from a context into a summary while answering abstractive questions involves paraphrasing the context using novel sentences. Yes-no question is a question whose expected answer is one of two choices, one that affirms the question and one that denies the question. Typically, the choices are either yes or no.
question:
Our data has been developed by crawling and pre-processing an OSG web forum. The forum has a great variety of different groups such as depression, anxiety, stress, relationship, cancer, sexually transmitted diseases, etc. 
 Question: How did they obtain the OSG dataset?
answer:
Extractive


question:
The CreateDebate dataset was collected from an English online debate forum discussing four topics: abortion (ABO), gay rights (GAY), Obama (OBA), and marijuana (MAR).  
 Question: What topics are included in the debate data?
answer:
Extractive


question:
We propose a simple and practical human evaluation for evaluating text summarization, where the summary is evaluated against the source content instead of the reference. It handles both of the problems of paraphrasing and lack of high-quality reference.  To avoid the deficiencies, we propose a simple human evaluation method to assess the semantic consistency. Each summary candidate is evaluated against the text rather than the reference. If the candidate is irrelevant or incorrect to the text, or the candidate is not understandable, the candidate is labeled bad. Otherwise, the candidate is labeled good. Then, we can get an accuracy of the good summaries. The proposed evaluation is very simple and straight-forward. It focuses on the relevance between the summary and the text. The semantic consistency should be the major consideration when putting the text summarization methods into practice, but the current automatic methods cannot judge properly. For detailed guidelines in human evaluation, please refer to Appendix SECREF6 .  
 Question: What human evaluation method is proposed?
answer:

Abstractive