Reinforcement Critical Subgraph Mining for Long Document Extractive Summarization
Authors: Cunli Mao, De Liu, Yongbing Zhang, Kaiwen Tan, Yuxin Huang, Zhengtao Yu


Reviewer paper summaries: To address the redundancy issues and improve coherence of summaries, the paper proposes a graph-based extractive summarization method for long documents. For each document, the paper constructs a hierarchical graph with nodes of words, sentences and documents. The paper then generates sentence representation based on the graph. Using the sentence representation, the paper also generates a global sentence representation and a sentence representation based on extraction history. Using these three types of sentence representations, the paper trains the critical subgraph extractor to extract summaries using policy gradients.
Summary of strengths: 1.	The graph proposed by this paper brings performance improvements compared to previous baselines based on automatic and human evaluation.

2.	The paper conducts comprehensive experiments to show the effectiveness of each module.
Summary of weaknesses: 1.	The description of the method is not clear (Sec 2.2.1, 2.2.2). 

In Sec. 2.2.1, the Graph Construction paragraph does not describe how the edges of the graph are constructed. For example, for the edges between sentences, clarification on whether there are edges between all sentences in the documents or there are only edges between sentences in the same paragraph or there are only edges between neighboring sentences is needed. 

In Sec. 2.2.2, the relationship between Equation 5, 6 and Equation 7,8 is not clear. The paper initially says that the representation of each node is updated following Equation 5, 6, however, it then says that the representation of each paragraph node is updated following Equation 7, 8. I am wondering whether 7, 8 overrides Equation 5, 6 for paragraph nodes. Besides, since there are paragraph nodes in the graph, which can transfer information between paragraphs, the effect of global context encoder should be more clearly justified.

2.	The writing of ablation studies and effect of graph type is not clear (Sec. 3.4).

For the ablation studies, more descriptions on the ablated model are needed. For the model w/o Heterogeneous Graph, clarification on whether it directly uses the initialized sentence representation as sentence representation is needed.

For the effect of graph type, description or reference for similar graphs, topic graphs, and discourse graphs is needed. The paper also needs to clarify whether the graph proposed by this paper is a discourse graph.

3.	For human evaluation (Sec. 3.5), the paper needs to provide more details on annotators, such as the number of annotators and how they are recruited. Besides, the sample size is relatively small, the paper also needs to perform statistical test for human evaluation. Besides, in Table 4, the meaning of ‘IF’ needs to be clarified.
Comments, suggestions and typos: None
Overall assessment scores: 2