MatInf: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization
Table 5: Comparison of summarization datasets. “#Token” indicates the average token numbers of a document and a summary for each dataset.
['Dataset', 'Lang.', 'Domain', '#\xa0Doc', '# Token Doc.', '# Token Sum.']
In MatInf-Summ, question description can be seen as an extended and specific version of the question itself, containing more detailed background information with respect to the question. Besides, the question itself is often a well-formed interrogative sentence rather than extracted phrases. Our task is to generate the question from the corresponding description (i.e., D\rightarrow Q). Note that our task itself can support many meaningful real-world applications, e.g., generating an informative title for user-generated content (UGC). Also, there is only one public dataset for summarization in Chinese to date. Our dataset can be used to verify the effectiveness of existing models and eliminate the overfitting bias caused by evaluation on merely one dataset.