Scientific Paper Summarization Using Citation Summary Networks

By Vahed Qazvinian, Dragomir R. Radev. International Conference on Computational Linguistics, 2008

320 citations recorded by the source corpus.

Quickly moving to a new area of research is painful for researchers due to the vast amount of scientific literature in each field of study. One possible way to overcome this problem is to summarize a scientific topic. In this paper, we propose a model of summarizing a single article, which can be further used to summarize an entire topic. Our model is based on analyzing others' viewpoint of the target article's contributions and the study of its citation summary network using a clustering approach.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/036c70cd07e4a2024eb71b2b1e0c1bc872cff107

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Citations in the corpus

LinkBERT: Pretraining Language Models with Document Links (2022, background): “Citation links are often used to improve summarization and recommendation of academic papers (Qazvinian and Radev, 2008; Yasunaga et al., 2019; Bhagavatula et al., 2018; Khadka et al., 2020; Cohan et al., 2020).”
GORC: A large contextual citation graph of academic papers (2019, background): “…sentiment (Athar and Teufel, 2012), identifying meaningful citations (Valenzuela et al., 2015), extracting key phrases (Caragea et al., 2014), and citation context-based paper summarization (Co-han and Goharian, 2015; Mitrovic and M¨uller, 2015; Qazvinian and Radev, 2008; Teufel et al., 2006).”
Interpreting informational cues: An explorative study on information use among prospective homebuyers (2009, unknown): “This key observation was then put to work by extracting the set of citation sentences to produce a summary of an article’s contributions (Qazvinian and Radev, 2008).”
Stylometric Analysis of Scientific Articles (2012, background): “We also found no gain from transductive training, but greater cost, with more hyperparameter tuning and a slower SVM solver.”
The ACL anthology network corpus (2009, methodology): “We have previously developed systems using clustering the similarity networks to generate short, and yet informative, summaries of individual papers (Qazvinian and Radev 2008), We also include a large set of scripts which use the paper citation network and the metadata file to output the auxiliary…”
Can We Automate Scientific Reviewing? (2021, background): “2019; Erera et al., 2019; Cohan et al., 2018a; Cachola et al., 2020b), (ii) or from a “reader view” that argues a paper’s summary should take into account the view of those in the research community (Qazvinian and Radev, 2008; Cohan and Goharian, 2017; Yasunaga et al., 2019a).”
Coherent Citation-Based Summarization of Scientific Papers (2011, methodology): “Qazvinian and Radev (2008) proposed a method for summarizing scientific articles by building a similarity network of the citation sentences that cite the target paper, and then applying network analysis techniques to find a set of sentences that covers as much of the summarized paper facts as…”
Efficient Attentions for Long Document Summarization (2021, background): “Summarizing long inputs has been investigated in many domains, including books (Mihalcea and Ceylan, 2007), patents (Trappey et al., 2009), movie scripts (Gorinski and Lapata, 2015), and sci-entiﬁc publications (Qazvinian and Radev, 2008).”
Text Summarization Techniques: A Brief Survey (2017, background): “Machine learning methods have been shown to be very effective and successful in single and multi-document summarization, specifically in class specific summarization where classifiers are trained to locate particular type of information such as scientific paper summarization [61], [76], [77] and bio”
From Extractive to Abstractive Summarization: A Journey (2019, methodology): “Unlike the techniques in [65, 66] which solely focus on the informativeness os individual sentences, this tech-”