Definition: In this task, you are given a paper review. Based on the review, your job is to identify language and generate "en" if the review is in English or generate "es" if the review is in Spanish. Note that URLs in the text have been replaced with [Link].
Input: - Strengths: Detailed guidelines and explicit illustrations. - Weaknesses: The document-independent crowdsourcing annotation is unreliable. - General Discussion: This work creates a new benchmark corpus for concept-map-based MDS. It is well organized and written clearly. The supplement materials are sufficient. I have two questions here. 1) Is it necessary to treat concept map extraction as a separate task? On the one hand, many generic summarization systems build a similar knowledge graph and then generate summaries accordingly. On the other hand, with the increase of the node number, the concept map becomes growing hard to distinguish. Thus, the general summaries should be more readable. 2) How can you determine the importance of a concept independent of the documents? The definition of summarization is to reserve the main concepts of documents. Therefore, the importance of a concept highly depends on the documents. For example, in the given topic of coal mining accidents, assume there are two concepts: A) an instance of coal mining accidents and B) a cause of coal mining accidents. Then, if the document describes a series of coal mining accidents, A is more important than B. In comparison, if the document explores why coal mining accidents happen, B is more significant than A. Therefore, just given the topic and two concepts A&B, it is impossible to judge their relative importance. I appreciate the great effort spent by authors to build this dataset. However, this dataset is more like a knowledge graph based on common sense rather than summary.
Output:
en