Enhancing Scientific Papers Summarization with Citation Graph
This paper discusses a new way to summarize scientific papers by looking at how they are connected through citations. It introduces a model that uses these connections to create better summaries and presents a large dataset to support this research.
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- 1 Our inductive setting also has the intention to test whether models trained in a large-scale citation graph has the ability to transfer to another citation graph.
- 2 We aim to help researchers draft a paper abstract by utilizing its references, rather than the papers citing it, different to the previous work.
- 3 The same expression always has different writing styles in different papers, and this relevant information will help the model to better understand the entire research community.
- 4 Given a dataset D, each document d can be represented as a sequence of words, and the objective is to generate a target summary Y by modeling the conditional distribution.
Introduction
Text summarization systems have broad application prospects despite limited exploration in domains like scientific papers. Generating a good abstract for a scientific paper is challenging because these papers are usually longer and full of complex concepts and domain-specific items.
Cohan et al. and Xiao and Carenini leveraged paper structure information to generate abstracts for scientific papers.
Researchers usually write an abstract of a paper by referring to some examples.
Text summarization systems have broad application prospects despite limited exploration in domains like scientific papers.
In the inductive setting, papers in the test set are from a totally new graph which means all test nodes cannot be used during training.
Research Question
Our inductive setting also has the intention to test whether models trained in a large-scale citation graph has the ability to transfer to another citation graph. We aim to help researchers draft a paper abstract by utilizing its references, rather than the papers citing it, different to the previous work.
Methodology
Green text indicates domain-specific terms shared in these papers, orange text denotes different ways of writing the same sentences, and blue text represents the definition of Weak Galerkin Finite Element Method. Reasonable use of the information of reference papers may help solve the scientific papers summarization task.
Study Design
Citing papers appeared after the source paper, so this task does not help a researcher draft an abstract when the paper has not been cited yet.
They are all about the topic Weak Galerkin Finite Element Method and are thus very similar in content, logic, and writing style.
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Results & Findings
Text summarization automatically compresses a document into a shorter version that preserves a concise description of the content. Most previous work focused on the News domain and achieved promising results using the neural encoder-decoder architecture.
- Text summarization automatically compresses a document into a shorter version that preserves a concise description of the content.
- Most previous work focused on the News domain and achieved promising results using the neural encoder-decoder architecture.
- Their methods dedicate to solving the problem of long document modeling and do not utilize the information of references.
- We highlight the importance of the citation graph and believe that it can assist in generating high-quality summaries.
- The same expression always has different writing styles in different papers, and this relevant information will help the model to better understand the entire research community.
We can improve the quality of summary for a paper with its citation information, but it cannot help authors to draft the summary while writing the paper.
The same expression always has different writing styles in different papers, and this relevant information will help the model to better understand the entire research community.
Practical Applications
The transductive division indicates that most neighbors of papers in test set are from the training set, but we introduce SNN (inductive) by splitting the whole citation graph into three independent subgraphs to consider real cases where test papers may come from a new graph. Meanwhile, it has the most sections, showing that our dataset retains the most complete paper structure possible.
Scientific Papers Summarization
The section outlines the history of automatic summarization for scientific papers, noting that most previous work focused on document content rather than citation relationships. It distinguishes between citation summarization and the proposed method, which utilizes reference papers as background knowledge.
Figures Explained
Frequently Asked Questions
Our inductive setting also has the intention to test whether models trained in a large-scale citation graph has the ability to transfer to another citation graph. We aim to help researchers draft a paper abstract by utilizing its references, rather than the.
Green text indicates domain-specific terms shared in these papers, orange text denotes different ways of writing the same sentences, and blue text represents the definition of Weak Galerkin Finite Element Method. To our best knowledge, this is the first large-scale scientific papers.
The same expression always has different writing styles in different papers, and this relevant information will help the model to better understand the entire research community. Given a dataset D, each document d can be represented as a sequence of words, and.
The overall loss for the whole sequence is given by the formula. The transductive division indicates that most neighbors of papers in test set are from the training set, but we introduce SNN (inductive) by splitting the whole citation graph into three.
Text summarization systems have broad application prospects despite limited exploration in domains like scientific papers. We can improve the quality of summary for a paper with its citation information, but it cannot help authors to draft the summary while writing the paper.
This paper discusses a new way to summarize scientific papers by looking at how they are connected through citations. It introduces a model that uses these connections to create better summaries and presents a large dataset to support this research.