  With the development of information technology, there is an explosive growth
in the number of online comment concerning news, blogs and so on. The massive
comments are overloaded, and often contain some misleading and unwelcome
information. Therefore, it is necessary to identify high-quality comments and
filter out low-quality comments. In this work, we introduce a novel task:
high-quality comment identification (HQCI), which aims to automatically assess
the quality of online comments. First, we construct a news comment corpus,
which consists of news, comments, and the corresponding quality label. Second,
we analyze the dataset, and find the quality of comments can be measured in
three aspects: informativeness, consistency, and novelty. Finally, we propose a
novel multi-target text matching model, which can measure three aspects by
referring to the news and surrounding comments. Experimental results show that
our method can outperform various baselines by a large margin on the news
dataset.
