Structured Neural Summarization
Summarization of long sequences into a concise statement is a core problem in natural language processing, requiring non-trivial understanding of the input. Based on the promising results of graph neural networks on highly structured data, we develop a framework to extend existing sequence encoders with a graph component that can reason about long-distance relationships in weakly structured data such as text. In an extensive evaluation, we show that the resulting hybrid sequence-graph models outperform both pure sequence models as well as pure graph models on a range of summarization tasks.
 This paper presents a structural summarization model with a graph-based encoder extended from RNN. Experiments are conducted on three tasks, including generating names for methods, generating descriptions for a function, and generating text summaries for news articles. Experimental results show that the proposed usage of GNN can improve performance by the models without GNN. I think the method is reasonable and results are promising, but I*d like to see more focused evaluation on the semantics captured by the proposed model (compared to the models without GNN). Here are some questions and suggestions: - Overall, I think additional evaluation should be done to evaluate on the semantic understanding aspects of the methods. Concretely, the Graph-based encoder has access to semantic information, such as entities. In order to better understand how this helps with the overall improvement, the authors should consider automatic evaluation and human evaluation to measure its contribution. Also from fig. 3, we can see that all methods get the "utf8 string" part right, but it*s hard to say the proposed method generates better description. - In the last table in Tab. 1, why the authors don*t have results for adding GNN for the pointer-generator model with coverage?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Structured Neural Summarization
Summarization of long sequences into a concise statement is a core problem in natural language processing, requiring non-trivial understanding of the input. Based on the promising results of graph neural networks on highly structured data, we develop a framework to extend existing sequence encoders with a graph component that can reason about long-distance relationships in weakly structured data such as text. In an extensive evaluation, we show that the resulting hybrid sequence-graph models outperform both pure sequence models as well as pure graph models on a range of summarization tasks.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 This paper presents a structural summarization model with a graph-based encoder extended from RNN. Experiments are conducted on three tasks, including generating names for methods, generating descriptions for a function, and generating text summaries for news articles. Experimental results show that the proposed usage of GNN can improve performance by the models without GNN. I think the method is reasonable and results are promising, but I*d like to see more focused evaluation on the semantics captured by the proposed model (compared to the models without GNN). Here are some questions and suggestions: - Overall, I think additional evaluation should be done to evaluate on the semantic understanding aspects of the methods. Concretely, the Graph-based encoder has access to semantic information, such as entities. In order to better understand how this helps with the overall improvement, the authors should consider automatic evaluation and human evaluation to measure its contribution. Also from fig. 3, we can see that all methods get the "utf8 string" part right, but it*s hard to say the proposed method generates better description. - In the last table in Tab. 1, why the authors don*t have results for adding GNN for the pointer-generator model with coverage?
assistant
 This paper presents a structural summarization model with a graph-based encoder extended from RNN. Experiments are conducted on three tasks, including generating names for methods, generating descriptions for a function, and generating text summaries for news articles. Experimental results show that the proposed usage of GNN can improve performance by the models without GNN. I think the method is reasonable and results are promising, but I*d like to see more focused evaluation on the semantics captured by the proposed model (compared to the models without GNN). Here are some questions and suggestions: - Overall, I think additional evaluation should be done to evaluate on the semantic understanding aspects of the methods. Concretely, the Graph-based encoder has access to semantic information, such as entities. In order to better understand how this helps with the overall improvement, the authors should consider automatic evaluation and human evaluation to measure its contribution. Also from fig. 3, we can see that all methods get the "utf8 string" part right, but it*s hard to say the proposed method generates better description. - In the last table in Tab. 1, why the authors don*t have results for adding GNN for the pointer-generator model with coverage?