1. A method comprising: identifying, by a processor, an original topic representation for a set of electronic documents stored at least temporarily in a non-transitory storage media; performing, by the processor, an informativeness analysis on the original topic representation, wherein performing the informativeness analysis on the original topic representation comprises: identifying a set of knowledge points in the original topic representation; calculating an informativeness feature of each knowledge point in the set of knowledge points; calculating a weighted informativeness value of the original topic representation based on the informativeness feature of each knowledge point in the set of knowledge points; in response to determining that the weighted informativeness value is below a first threshold, removing the original topic representation; and in response to determining that the weighted informativeness value is above the first threshold, keeping the original topic representation; performing, by the processor, a topic consistency analysis on the original topic representation; performing, by the processor, a topic redundancy analysis on the original topic representation in view of a second topic representation; and generating, by the processor, a refined topic representation based on the informativeness analysis, the consistency analysis, and the redundancy analysis.
1. A method comprising: identifying, by a processor, an original topic representation for a set of electronic documents stored at least temporarily in a non-transitory storage media; performing, by the processor, an informativeness analysis on the original topic representation, wherein performing the informativeness analysis on the original topic representation comprises: identifying a set of knowledge points in the original topic representation; calculating an informativeness feature of each knowledge point in the set of knowledge points; calculating a weighted informativeness value of the original topic representation based on the informativeness feature of each knowledge point in the set of knowledge points; in response to determining that the weighted informativeness value is below a first threshold, removing the original topic representation; and in response to determining that the weighted informativeness value is above the first threshold, keeping the original topic representation; performing, by the processor, a topic consistency analysis on the original topic representation; performing, by the processor, a topic redundancy analysis on the original topic representation in view of a second topic representation; and generating, by the processor, a refined topic representation based on the informativeness analysis, the consistency analysis, and the redundancy analysis. 2. The method of claim 1 , wherein identifying the original topic representation for the set of electronic documents comprises: crawling the set of electronic documents; extracting a plurality of knowledge points from the set of electronic documents; performing a topic model analysis on the knowledge points from the set of electronic documents; and selecting the original topic representation for the set of electronic documents based on the topic model analysis.